In recent years， rapid urbanization and global warming have led to frequent and severe rainstorm and flood disasters in the Sichuan-Chongqing region.This change will not only have a serious impact on the ecological environment and socio-economic development of the area， but also significantly increase the pressure on urban infrastructure and threaten the safety of people's lives and property.Therefore， it is particularly important to scientifically and accurately analyze the disaster risk of rainstorm and flood in Sichuan-Chongqing region in the past and future.This paper utilized daily precipitation data from 50 selected meteorological stations in the Sichuan-Chongqing region， precipitation data from 5 CMIP6 models， gridded population and economic data under Shared Socioeconomic Pathways （SSPs）， as well as DEM and land use remote sensing data.Firstly， using Taylor diagrams， quantitative indices （S）， and standardized anomaly sequences， the study evaluated the simulation performance of 5 individual CMIP6 models， an equal-weighted aggregation of 5 models （EWA-5）， and unequally-weighted aggregations of 5 models （UEWA-5） for five selected extreme precipitation indices.Then， by building a comprehensive risk assessment model of rainstorm and flood disaster based on disaster risk and vulnerability of disaster bearing body， the study conducted risk assessments， future projections， and comparative analyses of rainstorm and flood disasters during baseline （1995-2014） and future near-term （2025-2044） and long-term （2045-2064） periods under three different climate change scenarios （SSP1-2.6， SSP2-4.5， SSP5-8.5）.Results indicated： （1） The EC-Earth3 model performed best in simulating the five extreme precipitation indices， with correlation coefficients between simulated and observed values of 0.78 for R95p， 0.90 for RX1day， and 0.77 for RX5day.Overall， the simulation performance of UEWA-5 exceeded that of EWA-5.（2） During the baseline period， central Sichuan exhibited high values for the five extreme precipitation indices， followed by eastern Sichuan and Chongqing， while western Sichuan showed lower values.The year 1998 recorded peak values for all five indices， with a maximum single-day precipitation of 86 mm for RX1day and an intensity （SDII） value of 11.3 mm·d^-1.（3） In future periods， the five extreme precipitation indices display a spatial distribution characterized by higher values in central regions and lower values around the periphery.Higher levels of social vulnerability and radiative forcing correlate with larger values of extreme precipitation indices.Comparing the two future periods， values of the indices are larger in the long term， notably with R95p averaging 846.8 mm， an increase of 169.2 mm compared to the near term.（4） During historical periods， areas with higher comprehensive risk of rainstorm and flood disasters were concentrated in central Sichuan and downtown Chongqing.In the two future periods， the high and moderately high-risk areas in central Sichuan are expected to expand， while the moderate-risk areas will shrink.The range of low-risk areas in the western Sichuan Plateau will also decrease， and the risk levels in southern Sichuan and eastern Sichuan-Chongqing border areas will respectively decrease to moderate-low and low-risk zones.Comparing the two future periods， the range of moderately high and moderate-risk areas in central Sichuan is expected to expand， while southwestern Chongqing will transition to a moderate-risk area in the long term.Other regions will generally maintain their original risk levels.Changes in disaster risk levels in the Sichuan-Chongqing region are less pronounced with increasing social vulnerability and radiative forcing， especially in the western Sichuan Plateau and northeastern Sichuan， where changes in disaster risk levels are minimal.The study results can provide important references for reducing disaster risks， enhancing emergency response capabilities， and making scientifically informed decisions for disaster prevention in the Sichuan-Chongqing region.

Drought represents a significant contributing factor to global climate-related disasters.It not only endangers the stability of global ecosystems and biodiversity but also has far-reaching implications for socio-economic development.As global climate change intensifies， so too does the frequency and intensity of droughts.Drought events in ecologically fragile regions not only threaten the availability of water resources but also increase the risk of food insecurity， ecological degradation and social conflict.Nevertheless， despite the growing body of research in this area， there remain significant gaps in our understanding of the spatial and temporal characteristics of drought occurrences over the past four decades， as well as its evolutionary trends under different climate scenarios in the future.This study employs the Standardized Precipitation Evapotranspiration Index （SPEI） and CMIP6 climate change scenarios to analyze the spatial and temporal characteristics of global droughts over the past four decades and to predict the evolution of global droughts under different climate scenarios （SSP1-2.6、 SSP2-4.5、 SSP5-8.5） over the next 80 years.The findings of the study indicate that： （1） During the period between 1980 and 2022， there were notable variations in the spatial and temporal characteristics of global drought across different regions.Globally， approximately 57% of the land area does not exhibit a significant drought trend.However， about 33% of the land shows a persistent aridification trend， particularly in some already arid regions， where the intensity of drought has increased.Conversely， only 10% of the area is becoming wetter， indicating that the regions of the globe that are becoming drier are significantly larger than those that are becoming wetter.This suggests that the aridification process is spreading globally； （2） Over the past four decades， the globe has experienced an arid trend with no significant seasonal differences.However， the arid regions in winter are expanding， accounting for 33.2% of the global land area； （3） Different vegetation cover types exhibit varying responses to drought.Sparsely vegetated areas are more susceptible to drought， while densely vegetated areas tend to be wetter.Furthermore， arid climate zones situated within diverse climatic contexts are confronted with more pronounced drought-related challenges.The largest proportion of severe drought is observed in extreme arid zones， which account for up to 67% of the global land area， this indicates a higher frequency of drought events in drylands； （4） The probability of drought events is predicted to increase significantly in Africa， South America， southeastern Asia， and southern North America， particularly in tropical or warm climatic zones， extreme arid zones， and evergreen broadleaf climate zones.Furthermore， droughts are expected to become more frequent and severe， especially in tropical or very warm climate zones， arid zones， and broadleaf evergreen forest regions.The SSP5-8.5 scenario is projected to have the highest probability and intensity of drought events over the next 80 years， and will be challenged by more frequent and severe droughts.The findings of this study underscore the pervasive and severe nature of the global aridification trend， particularly in the context of climate change， where the frequency and intensity of droughts are projected to increase significantly.This trend not only enhances our comprehension of the risk of drought， but also furnishes an essential point of reference for policymakers， water managers， and the general public.In order to mitigate the potential intensification of droughts in the future， it is imperative that all sectors of society implement more proactive and efficacious measures to promote adaptation and mitigate the challenges posed by droughts.The rational management of water resources， improvements in agricultural irrigation techniques， enhanced ecosystem resilience and the strengthening of monitoring and early warning systems for climate change and droughts will ensure global ecological security and facilitate sustainable socio-economic development.

In an effort to elevate the precision of low-level wind shear forecasting， this paper amalgamates European Centre for Medium-Range Weather Forecasts （ECMWF） fifth-generation reanalysis data （ERA5） and National Centers for Environmental Prediction Final Operational Global Analysis （FNL） reanalysis data， high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model （ASTER GDEM） terrain data， and real-time observational data from Lanzhou Zhongchuan Airport.It employs the Weather Research and Forecasting Model （WRF）， WRF integrated with Computational Fluid Dynamics （CFD）， and Long Short-Term Memory （LSTM） neural network methods to simulate and analyze two wind shear events at Lanzhou Zhongchuan Airport on April 15-16， 2021.The findings reveal that： （1） within grids smaller than 1 kilometer utilizing Large Eddy Simulation （LES）， the WRF model demonstrates superior performance in wind speed simulation for individual stations， yet it falls short when compared to the WRF model combined with Computational Fluid Dynamics （CFD） models in simulating near-surface horizontal wind field wind speeds； （2） concerning the simulation of two low-level wind shears encountered during aircraft landing， both Weather Research and Forecasting Model - Large Eddy Simulation （WRF-LES） and Weather Research and Forecasting Model - Computational Fluid Dynamics （WRF-CFD） models are capable of simulating the first wind shear， however， the second appears to be influenced by the potentially lower wind speed data input into the models， with neither model achieving the threshold for wind speed difference， necessitating further validation in future work； （3） under low wind speed conditions （6 meters per second）， the LSTM-based single-variable wind speed prediction model maintains an average absolute error of approximately 0.59 meters per second， effectively capturing the nonlinear relationship of wind speed changes under various terrain and circulation background conditions.Despite being constrained by WRF errors and incomplete observational elements， multi-variable wind speed prediction can achieve wind speed forecasting with higher computational efficiency and generalization capabilities while ensuring that the average absolute percentage error is less than 6.60%.This paper not only verifies the differences between WRF-CFD and WRF-LES coupling schemes in wind field and low-level wind shear forecasting but also explores the feasibility and accuracy of LSTM-based wind speed prediction， aspiring to offer new perspectives and methods for enhancing wind field simulation accuracy and reducing the time required for detailed wind field simulation.

The freeze-thaw cycle of near-surface soil in the perennial permafrost region of the Qinghai-Xizang （Tibetan） Plateau plays a crucial role in regulating water and energy exchange between the soil and the atmosphere.Investigating its spatiotemporal characteristics and response to climate change is essential for understanding the mechanisms driving climate change on the plateau.In this study， we calculated near-surface freeze-thaw parameters-including the start and end times of soil freezing， thawing duration， and freezing duration-across the perennial permafrost region of the plateau from 1980 to 2017 using the Common Land Model 5.0 （CLM5.0）.We further analyzed their spatiotemporal variations and correlations with temperature， precipitation， snow depth， and vegetation index.The results show that： （1） The onset of near-surface soil freezing in the plateau’s permafrost region occurs between September and mid-to-late October， while the thawing period ends between February and May.Semi-humid regions have the longest thawing duration， whereas semi-arid regions have the shortest， with an average difference of 15 days.The freeze-thaw status of permafrost soil on the plateau exhibits significant changes.Except for areas near the Karakoram Mountains， most permafrost regions show a decreasing trend in freezing duration and an increasing trend in thawing duration.The average growth rate of soil thawing duration across the plateau is 2 d·（10a）⁻¹， with the most significant increase observed in semi-humid regions， reaching 4 d·（10a）⁻¹.（2） The freeze-thaw parameters of the plateau's permafrost are associated with geographical factors.In the latitude range of 29°N -36°N and longitude range of 82.5°E -103°E， the thawing duration shows an increasing trend； however， the rate of change decreases in some areas while increasing in others.Additionally， as elevation increases， the growth rate of thawing duration declines.（3） The duration of permafrost thawing is significantly correlated with snow depth， near-surface temperature， precipitation， and vegetation index， though these relationships vary across different climatic regions.Near-surface temperature exhibits a strong positive correlation across all regions， making it the primary driver of freeze-thaw changes.Precipitation and snow depth show positive and negative correlations， respectively， with particularly strong correlations in semi-humid areas.The vegetation index is positively correlated with thaw duration in all regions， with the strongest correlation observed in semi-arid areas.（4） The relationship between thawing duration and seasonal climatic factors varies.Near-surface air temperature exerts a significant influence on the freeze-thaw process at seasonal scales， with the most pronounced impact occurring in spring.Precipitation is positively correlated in summer but negatively correlated in winter.Both snow depth and vegetation index are significantly correlated with thawing duration in semi-arid and semi-humid regions during spring， exhibiting negative and positive correlations， respectively.（5） Near-surface temperature influences the freeze-thaw cycle in the plateau’s perennial permafrost region during both dry and wet seasons.However， the effects of snow depth， precipitation， and vegetation index are more pronounced during the wet season.

The Coupled Model Intercomparison Project （CMIP） provides reliable scientific data for predicting ecology， hydrology and climate under the backdrop of global change.However， there are large biases in current climate models， especially on the Qinghai-Xizang Plateau （QXP）.In this study， we employed Detrended Quantile Mapping （DQM） and Quantile Delta Mapping （QDM） methods to correct and evaluate the precipitation and temperature data of eight CMIP6 models with better simulation performance， utilizing the China Meteorological Forcing Dataset （CMFD）.The results showed that Both methods had corrected the simulation biases of the models， and the correction effects for temperature and precipitation data over the QXP were relatively consistent between the two methods.Then， based on the corrected multi-model ensemble mean （MME） results from QDM method， we analyzed the spatial and temporal variation characteristics of extreme high temperature events， low temperature events， atmospheric dryness and precipitation over the QXP in the early 21^st century （2015 -2057） and later 21^st century （2058-2100）.Under different emission scenarios in the future， extreme high temperature events strengthen， especially in the southeast of the QXP.Extreme high temperature events enhance with the increase of radiation.Extreme low temperature events decrease， with no occurrence in the later 21^st century under high emission scenarios （SSP370 and SSP585）.Under different emission scenarios， precipitation and saturated vapor pressure difference both exhibit a significant increasing trend on the QXP.With global warming， the increase of precipitation does not mitigate atmospheric drought.The atmospheric dryness increases significantly under the future scenarios， especially in summer， at 1.3 to 2 times compared to annual average.

Using effective bias correction methods and transforming non-stationary data to stationary can enhance the scientific accuracy of temperature analysis， allowing for a deeper understanding of its temporal and spatial distribution characteristics and evolution patterns.This study utilizes the ERA5_Land near-surface （2 m） monthly mean temperature observation dataset covering the period from 1970 to 2014.Initially， it employs the Taylor diagram， Taylor index， interannual variability skill score， and rank scoring method to evaluate and select among six climate models from the International Coupled Model Intercomparison Project Phase 6 （CMIP6） and the multi-model ensemble （MME） average models.Subsequently， the superior models are refined using the Delta bias correction method and the Normal distribution matching method.Finally， the study analyzes the temporal and spatial temperature variation characteristics of the Qinghai-Xizang Plateau from 2015 to 2100 under the SSP1-2.6， SSP2-4.5， and SSP5-8.5 scenarios.The results indicate that： （1） Among the six CMIP6 models and the multi-model ensemble （MME） average models analyzed in this study， the EC-Earth3 model demonstrates the most effective performance in simulating temperature.（2） When comparing the Delta bias correction results of the EC-Earth3 model with observational data， the regional averages of the coefficient of determination （R²） and the Nash-Sutcliffe efficiency coefficient （NSE） are 0.992 and 0.983， respectively.After applying the Normal distribution matching method for correction， the regional average values of R² and NSE are 0.990 and 0.978， respectively.This comparison reveals that the Delta bias correction method exhibits superior correction efficacy for the model's monthly temperature.（3） According to the combination of EOF-EEMD， the annual temperature of the first typical field of the three scenarios changes uniformly in the whole region， and there is a common sensitive area of temperature change under SSP1-2.6 and SSP2-4.5 scenarios， that is， the central region of the Qiangtang Plateau.The temperature dynamics in the second typical field reveal a gradual reverse-phase change from the upper reaches of the Zhaqu River to surrounding areas.Under the SSP1-2.6 scenario， the plateau experiences overall cooling in the east and warming in the west.Conversely， under the SSP2-4.5 and SSP5-8.5 scenarios， the plateau initially warms in the east and cools in the west， followed by a subsequent cooling in the east and warming in the west.This study provides a reference for bias correction methods that enhance the accurate application of climate model data in the Qinghai-Xizang Plateau region and offers essential foundational information for a comprehensive assessment of the impacts of temperature changes on water resources， ecosystems， and the environment in this area.

Based on the China snow depth time series data set and high resolution ground meteorological element driven dataset， this study analyzes the spatial and temporal variation of snow depth on the Qinghai-Xizang （Tibetan） Plateau by watershed and elevation gradient during the 1980 -2020 snow season considering different river basins and elevation gradients.Additionally， the study investigates the response of snow depth to climate change in the context of hydrothermal factors.The results show that： （1） Spatial difference in snow depth on the Qinghai-Xizang （Tibetan） Plateau was obvious， showing a distribution pattern of high in the west and low in the east， and greater in the high-altitude mountain areas than in the basin plains， with the average snow depth in the high-altitude mountain areas generally greater than 10 cm.The average snow depth decreased at a rate of 0.25 cm/decade， 64.74% of the regions showed a declining trend， with statistically significant decreases in 29.09% on the Qinghai-Xizang （Tibetan） Plateau during the snow season from 1980 to 2020.（2） There is a clear vertical zonation of snow depth and its trend as influenced by altitude.Below an altitude of 4.2 km， average snow depth increased with elevation.Between 4.2 km and 4.8 km， average snow depth decreased as elevation rises.Above 4.8 km， average snow depth again increased with elevation.A decreasing trend in snow depth was observed across all elevation bands， with the rate of decrease initially increasing and then decreasing with elevation， exhibiting a threshold at approximately 5.0 km.The most rapid decrease in mean snow depth ［3.36 cm·（10a）^-1］occurred in the 5.0~5.2 km elevation band.The interannual variation of mean snow depth exhibited a pronounced altitude-dependent pattern， the rate of snow depth reduction was significantly higher at higher elevations than at lower elevations， especially at 4.8~5.5 km.（3） Climate change on the Qinghai-Xizang （Tibetan） Plateau is ‘warmer and wetter’ overall， but ‘warmer and drier’ in the north-west and south during the snow season from 1980 to 2020.However， there are watershed differences and elevation differences in the response of snow depth to climate change.Specifically， in the Nujiang， Ganges， Amu Darya， and Indus River basins， the warming and aridification of climate conditions have contributed to a reduction in snow depth.Conversely， temperature has a more pronounced effect on snow depth in the Yarlung Tsangpo River， the interior plateau， as well as the Yangtze River basins， the Qaidam Basin， and the Tarim Basin.Additionally， precipitation plays a more significant role in influencing snow depth in the Yellow River， Heihe River basin.In regions with altitudes below 3.5 km， climate conditions characterized by warming and aridification have led to a reduction in snow depth.However， in areas with altitudes above 3.5 km， temperature has a more pronounced influence on snow depth.The altitude-dependent warming of temperature accounts for the altitude-dependent reduction in snow depth.

Global warming has led to rapid changes in the sea ice of the Antarctic and Arctic， triggering a number of climate feedbacks.Turbulent heat exchange between the polar seas and the air plays an important role in these feedbacks.Solar radiation， as the main energy source at the polar sea surface， is mainly used for sea ice melting and air-sea heat exchange， but the higher albedo of sea ice results in low radiation absorption.Air-sea heat exchange is influenced by temperature and humidity gradients between the sea surface and the atmosphere， with sensible heat dominating at the sea-ice edge and latent heat farther from the sea ice.In the Arctic， air-sea heat exchange is dominated by sensible heat， whereas in the Antarctic it is dominated by latent heat.The air-sea heat fluxes at the north and south polar seas vary seasonally.Sea ice can also inhibit air-sea heat exchange to some extent.Accurate parameterization of the turbulent heat exchange between the sea and the atmosphere in the sea ice regions is crucial for simulating air-sea interactions， however， in situ observations remain extremely rare due to limitations of the polar environment， and accurately representing the turbulent air-sea exchange in polar oceans remains a challenge.In the future， the network of air-sea flux measurements in polar seas should be strengthened， especially in the marginal ice zone， which are necessary and crucial for a deep understanding of the role of air-sea interactions in polar regions on global climate change， and for reducing the uncertainty in climate models.Secondly， the dynamics and thermal properties of ice must be fully considered to optimize the parameterization scheme or develop new models to improve the simulation accuracy.Furthermore， the influence of waves on the air-sea heat exchange in the sea ice region should be clarified to fill the research gaps.Finally， the contribution of the air-sea heat exchange to climate change in the polar regions should be evaluated further to improve the understanding of the role of polar oceans in climate change.

Utilizing the Anomaly Numerical-correction with Observations （ANO） based on historical observation data and anomaly integration， in conjunction with the ERA5 reanalysis data， a rectification test was conducted on the forecasting products of the Southwest Center WRF-ADAS Real-time Modeling System （SWMS in short）.This study evaluated the efficiency of the ANO method in enhancing short- to medium-term weather forecasts for meteorological quantities during a catastrophic regional heavy rainfall event over the complex topography from June 20 to 25， 2019.Results revealed that the SWMS model exhibited commendable predictability in the middle and upper troposphere， although its accuracy gradually reduced in the lower layers.After post-processing correction using the ANO method， all the predicted variables showed obvious improvements.The average Anomaly Correlation Coefficient （ACC） for the 500 hPa and 700 hPa geopotential height fields within the 72-hour integration increased by a range of 0.1 to 0.2， reaching approximately 0.8， while the 850 hPa geopotential height ACC showed a maximum enhancement of 0.6.Concurrently， the Root Mean Square Error （RMSE） for the corrected 700 hPa and 850 hPa geopotential heights exhibited significant reductions with an average decrease of 24% and 66%， respectively.The correction outcomes for temperature， specific humidity， and horizontal wind also displayed positive effects， which reveal the efficient correcting performance of the ANO method based on historical observation data in rectifying short- to medium-term numerical forecasts of the SWMS model over complex topography.

The Qinghai-Xizang Plateau summer monsoon is an important component of the Asian monsoon system， significantly influencing the energy and moisture cycles in the plateau and its surrounding regions.This study uses JRA-55 monthly reanalysis data from 1980 to 2020 and GPCC monthly precipitation data， combined with the Qinghai-Xizang Plateau Monsoon Index.Various statistical methods， including correlation analysis， regression analysis， composite analysis， and dynamic diagnostics， are used in this study.This paper focuses on the impact of the summer monsoon over the Qinghai-Xizang Plateau on water transport， such as precipitation， atmospheric circulation， and water budget.The results show that： （1） When the Qinghai-Xizang Plateau summer monsoon is strong （weak）， precipitation in the central and eastern parts of the plateau increases （decreases）.（2） From the perspective of water vapor transport， when the summer monsoon over the plateau is stronger， there is an anomalous anticyclonic circulation over central India， an anomalous westerly airflow to the south of the plateau， and the water vapor transport over the plateau is primarily dominated by the westerly water vapor transport channel.（3） Analysed in terms of moisture budget， when the Qinghai-Xizang Plateau summer monsoon is strong （weak）， moisture inflow at the southern and western boundaries of the plateau increases （decreases）， while moisture inflow at the northern boundary decreases （increases）， resulting in an increase （decrease） in regional net moisture budget.（4） The impact of the Qinghai-Xizang Plateau summer monsoon on moisture convergence/divergence is mainly driven by the contribution of the wind’s dynamic component， while the thermal component from moisture advection is relatively small.

On August 14， 2018， Typhoon Yagi （2018） moved northward and impacted Shandong Province of China， resulting in widespread rainstorm and heavy rainstorm.The total rainfall caused by the typhoon in Shandong presents a round-shaped distribution.Specifically， on August 14， an outer spiral rainband appeared on the typhoon periphery in southeastern Shandong， bringing short-term heavy rainfall and local heavy rainstorms.Due to the relatively small scale of this rainband， both numerical forecasting models and forecasters face challenges in predicting its rainfall accurately.To study the mechanisms of the outer spiral rainbands of Typhoon Yagi， the characteristics and causes of the spiral rainbands are investigated in this study by using radar data and the observations from ground-based stations， radiosonde stations and aircraft.Numerical experiments are also conducted based on the Advanced Research WRF （Weather Research and Forecasting） model and its Hybrid-3DVAR （three-dimensional variational） data assimilation system.The model adopts 12 km and 4 km one-way nested grids， with 44 vertical layers.The initial ensemble perturbation fields are generated by using a stochastic perturbation method， and the Ensemble Transform Kalman Filter （ETKF） method is used for the bias correction of ensemble forecast， providing flow dependent background errors for the Hybrid-3DVAR assimilation module.Comparative experiments with and without the Aircraft Meteorological Data Relay （AMDAR） data assimilation are conducted by adopting 100% flow-dependent error covariance and by using a 45-minute assimilation time window.The results indicate that the outer spiral rainbands are formed by the merging and development of several linear mesoscale convective systems （MCSs）.The outer spiral rainbands exhibit distinct characteristics of the linear MCSs with leading stratiform precipitation， i.e.， the linear MCSs consist of several convective cells with back-building convection.There are several stronger linear MCSs merging laterally into other linear MCSs.Broad stratiform echoes appear in the front （eastern part） of the linear MCS in its maturity stage， and the convection develops up to 10 km or more.There is a weak-echo transition zone between the strong convective line and the sub-strong stratiform echo region.Short-term heavy rainfall occurs along the linear MCS at the maturity stage.The water vapor of heavy rainfall mainly comes from the near-surface layer （below 850 hPa） around the typhoon， and the water vapor flux convergence is mainly concentrated near the wind field convergence line.Before convection initiation， the middle and lower levels over Shandong are thermally unstable with high temperature and high humidity， and the wind rotates clockwise with height， which favor the development of convective systems.As the typhoon slowly moves northward， downward intrusion of cold air appears at 500 hPa.Below 900 hPa， on the southeast of the typhoon over central Shandong there are local convergence between southwesterly wind and southerly wind， and between southerly wind and southeasterly wind.The convergence-induced dynamic uplift triggers the release of unstable energy， stimulating several local linear MCSs.The MCSs develop northward along the steering flow.The linear MCSs merge and strengthen for several times， and finally the elongated spiral rainbands occur.During the convection lifetime， the updrafts are noticeably stronger than the downdrafts.At the mature stage of the convective systems， dry and cold downdrafts appear in the lower levels in the front of the MCS.Convective systems at the heights above 600 hPa move rapidly eastward with the upper-air steering flow， leading to the gradual weakening and dissipation of the linear MCS.Assimilation of AMDAR can improve the typhoon track and wind field forecasts of the WRF model， as well as the dynamical triggering mechanism of convective systems.Thus， the occurrence of spiral rainbands in the typhoon periphery could be accurately forecasted.Furthermore， central Shandong is a mountainous region， so how does the topography influence the triggering and developing of convective systems？ What are the differences between typhoon outer spiral rainbands and the main body spiral rainbands？ What are the differences between outer spiral rainbands？ These issues deserve further studies.

Precipitation plays a critical role in the Earth's hydrological and energy cycles， significantly influencing the biogeochemical cycles and energy exchanges on the land surface.In the ecologically fragile region of the Loess Plateau， the spatial and temporal variability of precipitation has profound implications for both the ecological environment and socioeconomic development.Therefore， study on the spatial and temporal variations of precipitation in the Loess Plateau holds substantial theoretical and practical significance.This study utilizes daily precipitation data from 115 meteorological stations across the Loess Plateau and its surrounding areas， covering the period from 1959 to 2018.By employing methods such as Inverse Distance Weighting （IDW） interpolation and wavelet analysis， the study provides a comprehensive analysis of the spatial and temporal characteristics of precipitation over the past 60 years in the Loess Plateau.The results showed that： （1） The spatial distribution of precipitation in the Loess Plateau exhibits a clear "stepped" pattern， gradually decreasing from southeast to northwest.This distribution highlights a significant gradient where the southeastern regions receive more precipitation than the northwestern regions， with a similar trend of more rainfall in the south compared to the north.Furthermore， localized topography plays a crucial role in modulating precipitation， with higher elevations generally receiving more rainfall.（2） Under the influence of changes in the East Asian monsoon and atmospheric circulation patterns， the spatial distribution of precipitation from 1989 to 2018 differs significantly from that of 1959 to 1988.Specifically， the 200mm and 400mm isohyets have shifted northward， with a notable decrease in precipitation in the southeastern monsoon-dominated areas， while precipitation has increased in the non-monsoon northwestern areas.The monsoon marginal zone of the Loess Plateau is particularly sensitive to monsoon variability.The continuous weakening of the East Asian summer monsoon has diminished the capacity for moisture transport， further exacerbated by El Niño-Southern Oscillation （ENSO） warm events， both of which have contributed to reduced precipitation in the southeast.Conversely， changes in atmospheric circulation have led to increased precipitation in the northwest， resulting in a slight expansion of the semi-humid regions in the area.（3） Over the study period， precipitation in the Loess Plateau exhibits a fluctuating upward trend， indicative of a general tendency towards increased wetness in the region.This suggests a long-term shift towards more humid conditions， which could have significant implications for the region's ecological restoration and water resource management.（4） The interannual variability of precipitation in the Loess Plateau is characterized by oscillations on multiple time scales， specifically at 5-year， 7-year， 11-year， and 43~45-year intervals， with the 5-year cycle identified as the dominant periodicity.

As China’s “Central Water Tower” and vital ecological barrier， the Qinling Mountains’ temperature variability plays an important role in regional water conservation， ecosystem stability， and regional climate regulation.To evaluate the performance of statistically downscaled and bias-corrected Global Climate Models （GCMs） dataset （NEX-GDDP-CMIP6） in simulating observed temperature changes and further to project the future temperature variability over the Qinling Mountains， this study analyzes 8 NEX-GDDP-CMIP6 models against the CN05.1 observational dataset.The assessment focuses on the models’ ability to replicate observed annual mean temperature patterns， spatial trends， and temporal variability from 1961 to 2014.Furthermore， future temperature changes under the four Shared Socioeconomic Pathway （SSP） scenarios are projected for the period 2015 -2100.The results demonstrate that 8 models effectively capture the observed spatial pattern， warming trends distribution and interannual variability， with corresponding correlation coefficients of 0.90~0.92， 0.51~0.77， and 0.46~0.57 for 1961 -2014， respectively.The multi-model ensemble mean （MME） outperforms individual models， with correlation coefficients of 0.92， 0.65 and 0.74 for the three metrics.The MME indicates a persistent warming trend over the Qinling Mountains， with the stronger warming under the higher SSP scenarios.The warming trends are projected increase at 0.10 ℃·（10a）^-1 （SSP1-2.6）， 0.26 ℃·（10a）^-1 （SSP2-4.5）， 0.42 ℃·（10a）^-1 （SSP3-7.0）， and 0.57 ℃·（10a）^-1 （SSP5-8.5） for 2015 -2100.Notably， the warming exhibit altitudinal， zonal， and meridional dependencies， intensifying with higher elevation， latitude， and longitude.Relative to the reference period （1995 -2014）， the annual mean temperature is projected to increase by 0.65~0.97 ℃ in the near-term （2021 -2040）， 1.37~2.0 ℃ in the mid-term （2041 -2060）， and 1.39~4.46 ℃ by the end-century （2081 -2100） under the four SSP scenarios.The temperature changes are temporally consistent across the North and South Slopes over the Qinling Mountains and following with the entire regional average.However， the North slope warms more rapidly than the South slope， particularly under high-emission scenarios （e.g.， SSP5-8.5）， where North slope warming accelerates markedly.These findings provide critical insights for climate adaptation and ecological management in the Qinling Mountains.

Climate and land-use change are important drivers of variation in carbon storage within terrestrial ecosystems.Investigating the effects of climate and land-use change on carbon storage has practical implications for proposing adaptive management strategies for carbon sequestration in a changing environment.In this study， the InVEST model and the PLUS model were used to evaluate the spatial and temporal dynamics of carbon storage in the water conservation area of the Yellow River under the dual influence of climate and land-use change.The results showed that the land-use in the water conservation area of the Yellow River was dominated by grassland and forest from 1980 to 2020， accounting for 80 % of the total area of the basin， with an increasing trend in the area of forest land， grassland， watershed and construction land， and a decreasing trend in the area of other land-uses.The types of land-use transfer include unused land to grassland， grassland to forest land and cultivated land.From 1980 to 2020， the carbon storage in the water conservation area of the Yellow River showed an overall growth trend.The growth area of carbon storage was mainly located in the western and central regions， increasing by 573.5×10⁶ t， which was closely related to climate warming and humidification and ecological restoration.The urban expansion areas in the central and northern regions are the main areas of carbon storage reduction.In the future， under different land-use scenarios， the area of forest and grassland in the ecological protection scenario will increase significantly.From 2030 to 2050， under SSP119 and SSP245 scenarios， carbon storage will increase by 294.83×10⁶ t and 79.56×10⁶ t， respectively， under natural development scenarios， and carbon storage will increase by 364.8×10⁶ t and 151.95×10⁶ t， respectively， under ecological protection scenarios.Low emission and ecological protection scenarios are favorable for carbon storage increase.In the future， the increase in carbon storage will mainly come from grassland， conversion of unused land into forest and cropland， and conversion of unused land into grassland.The decrease in carbon storage is mainly related to the conversion of forest land into grassland and cropland.It can be seen that protecting forest and grass is an important measure to improve the carbon storage of regional ecosystem.The results can provide a scientific basis for adjusting the land-use structure and carbon sequestration of the ecosystem in the water conservation area of the Yellow River.

Hongjiannao Lake is the largest desert freshwater lake in China.In recent decades， the area of the lake has sharply decreased.The evaporation of the lake surface is the main factor consuming its water volume.Therefore， this paper aims to reveal the characteristics of evaporation changes and the mechanism of the driving factors.Currently， most studies on Hongjiannao Lake directly use or convert the evaporation data observed at meteorological stations， which have many missing and discontinuous data， and do not qualitatively and quantitatively analyze the meteorological factors influencing the evaporation changes of Hongjiannao Lake.To address these issues， this paper uses the data converted from meteorological stations， calculates the evaporation using the FAO （P-M） formula， and simulates the evaporation using the CLM-LISSS model to obtain the evaporation data of Hongjiannao Lake.Through comparison with the converted evaporation data from meteorological stations， it is found that the evaporation values and correlations simulated by the CLM-LISSS model are closer to the actual situation than the results calculated by the FAO （P-M） formula.The evaporation simulation results based on the preferred model showed that the average annual value of simulated evaporation of Hongjiannao lake from 1980 to 2018 was 1004.56 mm， and the M-K mutation test did not find the mutation year， and the overall trend was significantly upward （3.01 mm·a^-1）.The meteorological factors that have significant positive correlation with evaporation are air temperature， wind speed and downward long-wave radiation， and their correlation with evaporation and their own change trend pass the significance test of 95%.The sensitivity coefficient of evaporation to meteorological factors and the contribution of each meteorological factor to evaporation change were quantitatively analyzed by the formula calculation method and the perturbation analysis method of climate state respectively.The meteorological factors with greater contribution obtained by the two methods were significantly consistent with the correlation， and they were all air temperature， wind speed and downward long-wave radiation.However， the contribution ranking obtained by these two methods is slightly different and the contribution values of each factor are significantly different.This is mainly due to the fact that the change of evaporation is only caused by the change of a single factor， which reduces the influence of other driving factors， and effectively reduces the error between the change value of evaporation trend and the contribution sum of meteorological factors， from 128.40 mm （109.40%） to 56.83 mm （48.42%）.The perturbation analysis of climate state is superior to the formula calculation method in both mechanism and error.The results show that the contribution of meteorological factors to evaporation changes from large to small are downward long-wave radiation （71.47%）， temperature （59.83%）， wind speed （41.00%）， air pressure （1.54%）， downward short-wave radiation （-3.00%） and specific humidity （-22.43%）.

Based on the hourly precipitation data of 3454 stations with dense space in Sichuan Province and the high-precision grid elevation data， the characteristics of precipitation in flood season in 7 regions of Sichuan Province in recent 10 years were analyzed.The results showed that： （1） There were 3 maximum centers of rainfall in flood season in Sichuan Province： Ya 'an in the southwest of the basin， Anxian in the northwest of the basin and Yanbian in the south of Panxi area.Anxian was the center of heavy rainstorm， and the rainfall in flood season was mainly contributed by the weather process of R ₂₄ ≥100 mm.（2） Affected by the trend of the mountains and the steepness of the terrain， the morphology and isoline gradient of the large value area around the basin had obvious differences.And the larger the accumulated rainfall in flood season， the more the sites were concentrated on the windward slope of the mountains.（3） The degree of night rain gradually weakened from southwest to northeast， among which Panzhihua was the most significant area of night rain in flood season.（4）The daily distribution of R ₂₄ ≥25 mm heavy rainfall was closely related to topography， and the heavy rainfall stations were only distributed in the steep transition zone between the western basin and the plateau.In addition， the percentage of stations with hourly rain intensity ≥50 mm·h^-1 in the rainstorm days in the northwest of the basin was the highest.（5） Compared with persistent heavy rain， the site distribution of persistent heavy rain was more significantly affected by the windward slope topography.

This study， through comprehensive observations of the radiation field in the Everest National Nature Reserve （hereinafter referred to as "Everest"）， reveals the spatiotemporal variation patterns and potential impacts on the ecological environment and climate.By establishing a meteorological gradient observation network covering diverse ecological environments， including alpine shrubs， alpine wetlands， and alpine desert grasslands， and utilizing advanced radiation observation equipment combined with data quality control and automated processing， the study collected multi-year， continuous， high-precision measurements of four-component radiation fluxes.The results show the following： （1） The net radiation flux at Everest and alpine shrub sites exhibits an annual increase of 0.7 W·m^-2 on an interannual scale； （2） In terms of multi-year monthly average net radiation flux， the values at Everest and alpine shrub sites increase from 15 W·m^-2 in January， peak at 110 W·m^-2 in August， and then decrease to 14 W·m^-2 by December.The multi-year monthly average downward shortwave radiation flux rises from 210 W·m^-2 in January to a maximum of 375 W·m^-2 in June， followed by a significant drop to 230 W·m^-2 in July， remains relatively stable from July to October， then sharply declines from October， reaching a minimum of 200 W·m^-2 in December； （3） The multi-year summer daily average net radiation flux at Everest and alpine shrub sites rises from 0 W·m^-2 at 07:00 （Beijing Time， the same as after）， peaks at 530 W·m^-2 at 13:00， and declines to -110 W·m^-2 at 20:00.The multi-year summer daily average downward shortwave radiation flux increases from 0 W·m^-2 at 07:00， peaks at 860 W·m^-2 at 13:00， and drops back to 0 W·m^-2 at 21:00.In winter， the daily average net radiation flux rises from -120 W·m^-2 at 08:00， reaches a maximum of 370 W·m^-2 at 14:00， then decreases， reaching -120 W·m^-2 again at 20:00.The daily average downward shortwave radiation flux in winter rises from 0 W·m^-2 at 08:00， peaks at 840 W·m^-2 at 14:00， and falls to 0 W·m^-2 by 20:00； （4） There are significant differences in radiation flux among the stations， with the contrast being particularly prominent between the alpine wetland station and the other two stations.The annual average net radiation， annual average downward shortwave radiation， multi-year monthly average net radiation， multi-year monthly average downward shortwave radiation， and the daily average net and downward shortwave radiation in both summer and winter are all higher at the alpine wetland station than at the Everest and alpine shrub stations.The results of this study provide new insights into understanding climate change in high-altitude regions and offer essential data support for developing remote sensing monitoring technology， improving global climate models， and formulating environmental protection strategies in plateau areas.

Research on the characteristics of carbon and water fluxes and water use efficiency （WUE） in the alpine meadow ecosystem of the Qinghai-Xizang Plateau is of crucial significance for accurately assessing the carbon balance， water cycle， and carbon-water coupling of the alpine grassland ecosystem under the background of climate change.In this study， based on the observation data made using eddy correlation in the alpine meadow on the eastern Qinghai-Xizang Plateau from 2012 to 2017， from the Maqu observation point of the Zoige Wetland Ecosystem Research Station of the Northwest Institute of Eco-Environment and Resources， Chinese Academy of Sciences， we analyzed the changes in carbon and water fluxes and WUE during the growing season.By combining multiple stepwise regression and structural equation modeling， we explored the main driving factors of carbon and water fluxes and WUE during the growing season.The results indicate that： （1） The average annual net ecosystem CO₂ exchange （NEE）， ecosystem respiration （Re）， and total gross primary productivity （GPP） of the Maqu alpine meadow ecosystem over 6 years were -109.7， 798.6， and 908.3 gC·m^-2·a^-1， respectively， showing an overall carbon sink； the annual average evapotranspiration （ET） is 446.5 kg·m^-2·a^-1 the 6-year average water use efficiency （WUE） was 2.0 gC·kg^-1.（2） The daily variations of NEE and GPP during the growing season showed a distinct unimodal pattern， peaking around 14:00 （Beijing Time， the same as after）， while Re showed a relatively flat diurnal pattern， lower slightly at night than during the day； the daily variation of ET exhibited a unimodal pattern， with peak monthly and monthly accumulative values in July； WUE displayed an asymmetric "U" curve with the minimum value at 13:00 -14:00， showing significant daily and monthly variations in July and August.（3） During the growing season， both multiple stepwise regression and structural equation models confirmed the dominant role of temperature in controlling carbon fluxes and radiation in controlling ET.Temperature and radiation were identified as the main influencing factors for WUE during the growing season.

The Ya'an area has a typical "windward slope" and "trumpet" topography， the observation and study of cloud-precipitation in the Ya'an area has always been one of the hot issues of mountain meteorology in China.In this paper， using the observation data of Ka-band millimeter-wave cloud radar， K-band micro-rain radar and DSG4 laser raindrop spectrometer during the autumn rainy season in Ya'an area in 2023， the two types of cloud-precipitation in Ya'an during the autumn rainy period are studied and compared.The results show that （1） the frequency of warm cloud-precipitation （WCP） is higher than that of cold cloud-precipitation （CCP） during the autumn rainy period in Ya'an， but the intensity of precipitation is generally weaker and the cumulative rainfall is less.（2） In terms of vertical structure and macroscopic features， there are obvious differences between WCP and CCP： the radar echo reflectivity factor Z high-frequency region of CCP has a wider distribution and stronger echo， and the magnitude of the slope of Z high-frequency region at different heights reflects the difference between the ice-phase particles' growth mechanism and rate and that of WCP's liquid cloud raindrops' touch-and-go growth rate； CCP's linear depolarization ratio， LDR， increases abruptly near the zero-degree layer due to the melting of ice-phase particles.The mean Doppler velocity V_m has a smaller value in the high-frequency region and the velocity spectral width W has a larger value in the high-frequency region， suggesting a larger mean droplet scale and a wider concentration distribution.In terms of macroscopic parameters， the cloud base and cloud top of the CCP are significantly higher， the cloud layer is thicker， and the distribution of values is more dispersed.（3） In terms of microscopic characteristics， the raindrop spectra of the two types of cloud-precipitation are different with altitude： the concentration of small raindrops in the CCP increases and then decreases with decreasing altitude due to the melting of ice-phase particles and raindrops merging， whereas that in the WCP fluctuates due to the effects of evaporation， merging， water vapor transport， and updraft lifting altitude.The medium- and large-sized raindrop concentrations of both types of cloud-precipitation increase with decreasing altitude on the whole.In terms of the differences in raindrop concentrations at different heights， above 455 m， the concentration of raindrops of all grain sizes in WCP is almost lower than that in CCP； below 455 m， the concentration of small and large raindrops in WCP is higher than that in CCP， and the concentration of medium raindrops is lower.（4） Comparison of the raindrop spectra under different precipitation intensities showed that when the precipitation is weak， the WCP had lower concentrations of raindrops of all particle sizes than the CCP， but when the precipitation reached a certain intensity （rainfall intensity R \ge5 mm·h^-1）， the WCP produced more small raindrops and a small number of larger raindrops.The few extreme values of Z， LDR， V_m and W of WCP are larger than those of CCP at altitudes below 3 km due to the synergistic effect of low-altitude rapids and topographic uplift in the region， which makes the small WCP raindrops repeatedly touch and form medium-to-large raindrops in the re-uplift airflow.

The weather and climate of the Qinghai-Xizang Plateau （QXP） not only exert profound influences on Asian regional climate but also regulate broader Northern Hemisphere climate patterns.Various global atmospheric reanalysis datasets developed abroad have been extensively utilized to characterize the QXP's climate features.In 2020， the China Meteorological Administration released its first-generation global atmospheric reanalysis product （CRA）， which demonstrated superior performance in regions with dense conventional observations， yet its skill over the QXP remains unclear.In this study， we performed an independent evaluation of CRA's upper-air temperature， wind fields， and relative humidity using 686 high-quality， non-assimilated radiosonde profiles from five stations collected during June-August 2014 in the 3rd Qinghai-Xizang Plateau Atmospheric Scientific Experiment.A non-independent assessment was also performed using routine observations from 21 sounding stations across the QXP and adjacent areas.Results were compared with those from ERA-Interim， ERA5， and JRA-55 reanalysis datasets.Non-independent validation results demonstrate that in the troposphere and lower stratosphere， correlation coefficients of temperature and wind speed between CRA and operational radiosonde observations exceed 0.9.Relative to operational radiosondes， CRA temperature bias of 400 hPa and 500 hPa approaching 0 ℃.The near-surface zonal wind RMSE of CRA is about 2.5 m·s^-1， decreasing gradually with altitude to 1.5 m·s^-1 at 100 hPa.Relative humidity RMSE remains below 20% across all altitude layers.Independent validation results indicate that errors in air temperature and wind speed over the eastern Qinghai-Xizang Plateau are generally smaller than those over the western plateau， whereas relative humidity errors are larger in the east.From 600 hPa to 30 hPa， the mean root-mean-square errors （RMSE） of CRA relative to radiosonde for temperature， zonal wind， and meridional wind are 1.38 ℃， 3.19 m·s^-1， and 3.22 m·s^-1 in the western Qinghai-Xizang Plateau， respectively； and 1.16 ℃， 2.65 m·s^-1， and 2.90 m·s^-1 in the eastern Qinghai-Xizang Plateau， respectively.Errors in the western QXP were slightly larger than in the east and exhibited pronounced diurnal variability.Maximum temperature and relative humidity errors below 600 hPa occur in the afternoon.At 500 hPa， peak relative humidity errors appear in the evening.CRA objectively reproduces the QXP’s vertical structures of temperature， wind speed， and relative humidity.Compared to other reanalysis products， CRA’s relative humidity estimates were closest to radiosonde observations， and wind field errors were slightly higher than those of other datasets but did not exceed 0.4 m·s^-1 on average.

The Qinghai-Xizang Plateau （QXP）， recognized as a global climate hotspot and sensitive indicator， profoundly influences regional and global climate and water cycles through complex multi-sphere interactions.This study presents comprehensive review of the current status and data resources of the Qinghai-Xizang’s multi-sphere observation network.Synthesizing multi-source observational data， it comprehensively reviews the key characteristics and mechanisms of the climate “warming and wetting” trend and its multi-sphere responses （atmosphere， cryosphere， hydrosphere， ecosystems）.Key findings reveal an accelerated warming rate， increased precipitation with distinct geographic variations， and subsequent chain reactions including thawing permafrost， melting glaciers， expanding lakes， enhanced vegetation growth， and increased extreme events.However， critical gaps persist in the current observation system， notably sparse coverage in the western Qinghai-Xizang， insufficient multi-sphere synergy， and imperfect data-sharing mechanisms.To address these challenges， future priorities should include expanding western Qinghai-Xizang's monitoring networks， promote low-cost automated instrumentation， enhance multi-source data fusion and model assimilation， and establish secure and standardized data-sharing platforms.This work advances our understanding of the Qinghai-Xizang's climate complexity and provides actionable insights for optimizing climate-environment monitoring systems.

Based on the hourly precipitation and 10 m wind speed data from the European Centre for Medium-Range Weather Forecasts （ECMWF） Fifth Generation Atmospheric Reanalysis dataset from 1979 to 2023， spatiotemporal changes and its corresponding clustering characteristics of compound precipitation and wind speed extremes （PWEs）， and the circulation characteristics in different periods in China were studied by using compound extreme events definition， trend analysis， spatial statistical analysis， and composite analysis.The results showed that PWEs in China were generally more frequent in the east than in the west.Among the PWEs in each subregion， the highest value was found in East China， where the mean value of the frequency and the days were the most in the range of 4~8 times and 4~8 d， and the corresponding area share reached 78.9% and 71.5%， respectively.The overall trend of PWEs from 1979 to 2023 had been decreasing， with the rate of change from 2011 to 2023 being 2.3 times and 3.4 times that of 1979 to 2010.The trend of PWEs from 1979 to 2010 showed an increasing trend in the central and eastern region of Eastern China， the central region of Southwest， and the northern region of Northwest， and the fastest decrease in Central China.From 2011 to 2023， positive trend values were mainly concentrated in the central region of China， and the Eastern China was the region of the fastest growth with rates of 0.96 times and 1.12 d per decade.In contrast， Southern China exhibited a decrease at rates of 0.81 times·（10a）^-1 and -0.77 d·（10a）^-1.The hot spot areas were concentrated on the west side of the Hu Line and coast region from 1979 to 2010， and the distribution of hot spot areas from 2011 to 2023 were consistent with the positive distribution of trend change.In addition， PWEs are the result of the combined effects of the high， middle， and low-level atmospheric layers.The enhancement of atmospheric high-level divergence and the weakening of the jet belt promote the upward movement of the atmosphere and the westward extension of the west Pacific subtropical high.The anomalous easterly wind in the middle atmosphere is conducive to the entry of water vapor from the periphery of the Northwest Pacific subtropical high into the inland regionof China， and the anomalous southeast wind in the low-level atmosphere further promotes the transport of water vapor to the inland region of China.The atmospheric circulation characteristics after 2010 also showed the development of PWEs events towards inland region of China.

Snow cover is an important component of the cryosphere.In recent years， climate warming has led to a reduction in snow cover area.This change may cause uneven distribution of water resources and a decline in biodiversity， thereby affecting local life， economic development and ecological environment.Qinghai Lake is the largest inland lake in China.In recent years， its water level has changed rapidly.The runoff into the lake is affected by the snow cover and its changes in the basin.However， the characteristics， changes and causes of the influence of the snow cover in the Qinghai Lake Basin are still unclear.Based on the temperature and precipitation data from the Moderate-resolution Imaging Spectroradiometer （MODIS） daily cloudless 500 m snow area product Dataset and the China Meteorological Forcing Dataset （CMFD）， This paper studies the spatial and temporal variation characteristics and influencing causes of snow cover in the Qinghai Lake Basin.The results show that： （1） There is a good correspondence between the distribution of the average annual snow cover frequency from 2000 to 2020 and the altitude.As the altitude decreases， the snow cover frequency also decreases accordingly.It is simultaneously affected by the average annual temperature and the annual precipitation.Among them， the areas significantly affected by the average annual temperature in a partial correlation are mainly distributed in the northern and eastern parts of Qinghai Lake， and the areas significantly affected by the annual precipitation in a partial correlation are mainly distributed in the middle and upper reaches of the Buha River in the middle of the Qinghai Lake Basin.（2） From 2001 to 2017， precipitation increased in the Qinghai Lake Basin and the Qilian Mountains region.However， due to the increase in the average annual temperature and the decrease in the annual snowfall in the two regions， the snow cover area decreased.（3） The intra-annual variations of snow cover area in the Qinghai Lake Basin and the Qilian Mountains region are relatively similar， both showing a double-peak fluctuation feature.However， the reduction in snow cover area from April to July and the increase from August to January of the following year in the Qinghai Lake Basin are both greater than those in the Qilian Mountains region.From January to March， the snow cover in the Qilian Mountains region decreased， while that in the Qinghai Lake Basin increased， which corresponds well to the lake effect after the melting of Qinghai Lake.

Climate comfortableness is the key factor that has impact in many fields， such as residents’ life quality， tourism development， and urban planning layout.The Universal Thermal Climate Index （UTCI is currently the most important and effective way to evaluate climate comfortableness at the international level.In-depth research on the climate comfortableness of the Yellow River Basin can not only fill the gap in the study of climate comfortableness in the Yellow River Basin area but also supplement a comprehensive understanding.Based on the results of climate zoning， the Yellow River Basin is divided into six sub-regions Using the reanalyzing data of ERA 5， the spatial distribution and temporal change of climate comfortableness in the Yellow River Basin from 1979 to 2022 were analyzed and discussed with the adoption of UTCI.The results show as follows： （1） From an overall perspective， the annual average UTCI value of the Yellow River Basin is 2.8 ℃， with a comfortable grade of coolness.The UTCI value is mostly in the cold zone and comfortable zone.The distribution of hot zone is relatively less.There is a large difference in UTCI distribution among the internal regions.Region I has a relatively longer duration of low temperature， and the area， with mild cold stress （coolness） and stronger cold stress （uncomfortable coldness）， is larger.Region II is mostly in the cold zone.Region III and Region IV are relatively close， and are dominated by “comfortableness” and “coolness”.The UTCI values in region V and region VI are at a higher level， but most areas are still in the comfortable zone.（2） In terms of the seasons， the four regions of III， IV， V， and VI have relatively extensive comfortable zones in spring and autumn， the overall comfortable area will be expanded in summer across the entire Yellow River basin， and the cold and uncomfortable zone will become dominant in winter， while the overall comfortable zone will be shrinked across the entire Yellow River basin.（3） The average UTCI in China as a whole has shown an overall upward trend from 1979 to 2022， with a change rate of 0.4 ℃·（10a）^-1.The range of change in sub-regions is 0.14~0.85 ℃·（10a）^-1.The annual UTCI change in the Yellow River basic shows a significant feature of west-high-east-low and north-high-south-low in the spatial distribution.（4） The level of climate comfortableness， taken as a whole， is mainly in the comfortable and slightly uncomfortable categories.The number of days in each of the six comfortable levels is as follows： 24 days （cold discomfort）， 126 days （slightly cold， discomfort）， 59 days （cool）， 131 days （comfort）， 19 days （slightly hot discomfort）， and 6 days （hot discomfort）.Region I and Region II have not been affected by the discomfort caused by heat.However， regions in the Yellow River basin， and regions of III， IV， V， and VI， are affected by slightly hot discomfort and the average duration in the slightly hot discomfort zone throughout the year is 19 days， 23 days， 24 days， 46 days， and 60 days respectively.

The seasonal vegetation greening in the Three River Source Region （TRSR） has a profound impact on the regional ecological environment and water resource security.In this study， the moisture drivers of seasonal vegetation greening in the TRSR and their responses to climate change were investigated using multi-source data from 2003 to 2021， through the application of trend analysis， correlation analysis and partial information decomposition （PID） analysis.The results showed that： （1） From 2003 to 2021， the linear trend of the Leaf Area Index （LAI） generally increased in spring， summer and autumn in TRSR， although the environmental conditions varied significantly between seasons.Linear trends in precipitation， soil moisture （SM） and snow cover （SC） all showed an increasing trend in spring and autumn， with insignificant changes in temperature.In summer， linear trends of temperature were slightly increased， but precipitation and SM slightly decreased， as well as insignificant changes in SC.（2） In terms of the effects of moisture driving factors on LAI： correlation analyses results showed that LAI was significantly positively correlated with SM in spring and summer， but not in autumn.The correlation between LAI and SC was weak in all seasons.By introducing the PID analysis method， the nonlinear and synergistic effects of SM and SC on LAI were effectively revealed.The independent information contribution of SC to LAI changes was higher in spring and autumn， making it the main moisture driver in these seasons， while SM contributed more in summer.At the same time， the synergistic effects of SM and SC played an important role in the changes of LAI in all seasons， with the synergistic information contribution exceeding 30% in all seasons.（3） Response of moisture drivers to climate change： correlation analyses results showed that SM was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in spring； SC was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in both spring and autumn.PID analyses also indicated that precipitation was the main meteorological factor influencing changes in SM and SC across the three seasons， with a higher independent contribution than temperature.However， the synergistic effects of temperature and precipitation on SM and SC in all seasons should not be overlooked.

On August 13-14， 2022， an extreme rainstorm event occurred in Yuzhong region of Gansu Province， northeast of the Qinghai-Xizang Plateau.Accumulated daily precipitation reached 130.6 mm and the maximum hourly precipitation was 36.6 mm， breaking the heaviest daily precipitation records of the region and causing serious social impact and economic losses.Based on the data of surface minute observation and high altitude observation， Lanzhou Doppler radar and ECMWF Reanalysis v5（ERA5）， by analyzing the observation characteristics， environmental conditions， topographic effects and instability mechanism of the two heavy precipitation stages in this extreme rainstorm， the results show that： （1） The rainstorm was caused by the convergence of weak cold air brought by the shortwave trough in the westerlies and warm and humid air outside the subtropical high in the Longzhong area.The 700 hPa shear line provided the dynamic lifting conditions， and the surface cold front provided the triggering conditions.（2） The radar reflectance factor in the rainstorm process was characterized by persistent strong echoes accompanied by "backward propagation"， low-level jets and obvious convergence.In the second stage， the echo top height behind the cold front was similar to that in the first stage， but the scope was larger and the structure was more compact， and the convective cloud development was more vigorous.（3） The water vapor conditions of the rainstorm were abundant.In the first stage， there was significant convective instability due to strong convergence and upward movement at the lower level and high convective effective potential energy.In the second stage， the upward movement was weakened， the convective effective potential energy was 0， and the dynamic and convective instability conditions were weak.（4） The release of unstable energy triggered by cold front baroclinic frontogenesis was the main triggering mechanism of precipitation in the first stage.After the transit of the cold front， the precipitation in the second stage was formed by the combination of terrain， frontogenic secondary circulation and instability.Since the heavy precipitation after the summer cold front was not common in the northeast part of the Qinghai-Xizang Plateau， forecasters tended to ignore such kind of rainstorms.Therefore， we need to strengthen monitoring and early warning of such rainstorm events.

This study aims to improve the accuracy of simulating soil hydrothermal processes on the Qinghai-Xizang Plateau by introducing a novel soil stratification method combined with an integrated freeze-thaw gravel parameterization scheme.The region's unique topography and complex climate pose challenges for conventional numerical models in achieving precise simulations.The proposed scheme incorporates freeze-thaw parameterization， gravel parameterization， and refined vertical soil discretization， offering a more comprehensive representation of the soil characteristics and terrain complexity specific to the Qinghai-Xizang Plateau.To evaluate the effectiveness of the scheme， the BCC-CSM atmospheric circulation model， provided by the National Earth System Modeling Center， was used for testing.The results demonstrate that integrating freeze-thaw and gravel parameterization significantly improves the representation of soil hydrothermal distributions， especially during the winter and at greater soil depths.By refining the soil stratification to 20 and 30 layers， the simulations of soil temperature and moisture have been further enhanced.The 30-layer stratification yields the most accurate outcomes， followed closely by the 20-layer configuration.This approach notably reduces bias and root mean square error in soil temperature simulations， particularly in the central and western regions of the Qinghai-Xizang Plateau， with better performance in winter compared to summer.While soil moisture simulation accuracy lags behind temperature results， the stratification refinement reduces errors， particularly in shallow soil layers.The enhanced stratification also improves the correlation between simulated values and CRA data， strengthening the alignment between simulation and observation， especially in the central and western parts of the plateau.This research provides new insights into soil hydrothermal processes on the Qinghai-Xizang Plateau and offers critical methodology and technical support for future climate simulations and predictions.Moreover， the proposed integrated scheme holds significant potential for simulating soil hydrothermal processes in other plateau regions and may be applied across a wide range of fields.

This study evaluates the simulation capabilities of 16 models with varying resolutions from the High-Resolution Model Intercomparison Project （HighResMIP） in reproducing warm-season （May to September） precipitation over the eastern slope of the Qinghai-Xizang Plateau， using the CN05.1 dataset as observational reference.Through comparative analysis of outputs from multiple models against observational data， this study elucidates model strengths and limitations in capturing spatiotemporal variability， precipitation intensity， and terrain-related mechanisms.Results indicate that high-resolution climate models demonstrate reasonable accuracy in simulating annual and warm-season precipitation spatial patterns， though notable inter-model discrepancies persist.Three models （CMCC-CM2-HR4， CMCC-CM2-VHR4， FGOAL-f3-H） successfully replicate the observed increasing trend in annual precipitation， while others exhibit stable or decreasing trends.Persistent model deficiencies emerge in simulating precipitation frequency and intensity： all models systematically underestimate light precipitation events （<1 mm∙d^-1） while overestimating heavy precipitation frequency （>4 mm∙d^-1）.Medium-to-low-resolution models show a systematic phase lag of approximately 30 days in diurnal precipitation cycles compared to observations.In contrast， the high-resolution models performed better in simulating precipitation frequency than the medium low resolution group.Based on comprehensive evaluation of temporal distribution， frequency characteristics， and model skill scores， the ECWMF model demonstrates superior performance， whereas the FGOAL-f3-H model exhibits significant negative biases.

The most significant meteorological component is temperature， and weather forecasting relies heavily on how accurately temperatures are predicted.This study uses a linear non-graded regression method to rectify the inaccuracies in temperature forecasts induced by terrain variation in the 2 m temperature hourly forecast product of the mesoscale numerical model （China Meteorological Administration Guangdong， CMA-GD）， and use the one-dimensional Kalman filtering method and the double-weighted moving average method to correct the results.The accuracy of the hourly distribution exhibits a diurnal variation feature， and the model terrain height deviation is linearly negatively connected with the temperature error mean value， according to the results.The daytime correction impact is superior than the nighttime correction effect following the ungraded regression method.recorrecting using the best time frame for mathematical correction techniques （15 days for the Kalman method and 20 days for the mean method）.It is discovered that the mean method's re-correction effect outperforms the Kalman methods， and that the correction effect is more pronounced during the day than at night.Summer and autumn have a better re-correction impact than winter and spring， with some negative correction effects in spring and little difference between the two techniques in the latter.In the former， the mean value method outperforms the Kalman method.There are eight stations with negative correction following the ungraded regression method， but no negative correction stations follow the mathematical correction methods.Therefore the northern region typically experiences a better corrective impact than the southern region.The fraction of correction magnitude for both MAE and ACC is positively correlated with a binomial connection.The terrain deviation correction method has the least slope and restricted correction effect， while the mean value approach has the best correlation and largest slope.An error assessment was conducted in the middle part of Poyang Lake Plain and the south Zhejiang-Fujian hilly region.The peak error value in the former was lower than that in the latter， and the correction amplitude at the peak was smaller.After correction， the MAE decreased by 25.1% and 19.8%， respectively.From November 2022 to January 2023， during frequent cold air intrusions， the MAE in the middle part of the Poyang Lake Plain decreased by 13.5%.With corrected forecast errors oscillating around the zero axis and a noticeable improvement in systematic positive errors， the model significantly overestimates the temperature forecast for high mountain areas.The temperature forecast errors oscillate with the smallest amplitude from August to October and the largest amplitude in spring and winter.Taking the warming process （May 1-6， 2022） and the strong cooling process （November 28-December 3， 2022） as examples， the corrected MAE decreased by 18.2% and 16.0%， respectively， indicating that the method has achieved stable correction effects during transitional weather.This composite method has good stability， strong forecast correction ability， easy to promote.

Soil water-heat dynamics are pivotal in influencing regional hydrological processes.Understanding the dynamics of soil thermal and moisture changes during freezing and thawing processes is essential for assessing water balance in high-altitude regions.This study utilizes meteorological and soil water-heat observational data from a typical shallow mountainous catchment in the Qilian Mountains to simulate the water-heat dynamics of subalpine shrub meadow soil using the SHAW model， analyzing the changes in water balance during the soil freezing and thawing process.The results indicate that the SHAW model effectively simulates the temporal and vertical variations in soil temperature and moisture in subalpine shrub meadow soils.The findings demonstrates that the Nash-Sutcliffe Efficiency （NSE） for simulated soil temperature at various depths exceeded 0.88， with ae correlation coefficient （R） greater than 0.97and a Root Mean Square Error （RMSE） less than 1.89 ℃.For soil moisture， the correlation coefficient （R） was greater than 0.94， NSE was greater than 0.88.and the RMSE was less than 0.05 m³·m⁻³.Overall， the simulation of soil temperature is more accurate than that of soil moisture， especially in deeper soil layers.The soil freezing and thawing periods， delineated by temperature profiles， revealed a distinct unidirectional freezing and thawing characteristic of the subalpine shrub meadow soil， with the longest duration in the complete freezing period and the shortest in the freezing period.The trends in temperature and moisture across the soil profile exhibit a "U" shape， indicating higher soil temperatures and moisture during the thawing period compared to the freezing period， with significant fluctuations in surface soil moisture and relative stability at deeper layers.The water balance characteristics are significantly varied across different soil freezing and thawing periods.During the freezing period， the precipitation input is 4.28 mm， with the main expenditure of water is deep percolation at 9.06 mm.In the complete freezing period， the precipitation input is 28.69 mm， with the main expenditure of water is surface runoff at 17.90 mm.During the thawing period and the complete thawing period， the precipitation input is 106.29 mm and 207.31 mm respectively， with the major water output through evapotranspiration， where plant transpiration accounted for 78.11% and 71.54% respectively.The soil moisture shows a negative balance during the freezing and complete thawing periods， indicating a net loss of moisture.Conversely， the soil moisture exhibits a positive balance during the complete freezing and thawing periods， signifying a net increase in moisture.This study may provide empirical data and theoretical support for the formation and transformation of water resources in the Qilian Mountain region.