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 Del‐ ta 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 Nor‐ mal 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 ac‐ curate application of climate model data in the Qinghai-Xizang Plateau region and offers essential foundational in‐ formation for a comprehensive assessment of the impacts of temperature changes on water resources，ecosystems，and the environment in this area.