Weather stations can provide high-accuracy local precipitation information, but individual stations usually have different time series, which may have a significant influence on the precipitation trend analysis and relevant studies. This impact may be particularly severe in the Qinghai-Tibetan Plateau, where the stations are very sparse and are hard for operations. The number of available China Meteorological Administration (CMA) stations decreased from 146 to 130 in the central and eastern Qinghai-Tibetan Plateau during 1979—2015, mainly due to the deactivation of some stations and the change of station types. In this study, an upscaling theory method based on the Bayesian linear regression was used to establish the mathematical relationship for precipitation value between a station with missing data and its adjacent stations with available data. The method is then used to interpolate and extend the monthly precipitation time series. It was constructed the time series of monthly precipitation at 148 stations in the central and eastern Qinghai-Tibetan Plateau and its surrounding areas during the period of 1979-2015. Cross-validation, using 29 time series complete stations, displays the constructed time series after interpolation and extension can generally restore the seasonal variation of the precipitation at stations with missing data. The new method is superior to several commonly used interpolation methods to a certain extent, including inverse distance weighted (IDW), local polynomial (LP), and kriging method. To illustrate the value of reconstructed precipitation data, two preliminary applications of the data were introduced, including satellite precipitation correction and regional precipitation trend analysis. The fusion of satellite precipitation (Tropical Rainfall Measurement Mission, TRMM) and gauge precipitation after interpolation and extension, indicates that the introduction of interpolation stations data can change the local precipitation distribution characteristics. To a certain extent, increasing the number of available stations helps to improve interpolation accuracy of grid precipitation. The interpolation and extension are helpful to quantify the spatial distribution and the temporal variation of precipitation in central and eastern Qinghai-Tibetan Plateau. Improving the precipitation grid interpolation accuracy in particular, the constructed time series then demonstrates that annual precipitation decreased significantly in the Southeast Qinghai-Tibetan Plateau after about 1998 but jumped slightly to a higher-level in the Northeast Qinghai-Tibetan Plateau since 2002, while no decadal change is seen in the transitional zone between the Southeast and Northeast Qinghai-Tibetan Plateau. This spatial difference in precipitation can roughly explain the spatial pattern of regional water cycles (glacier mass balances, lake water volume changes, and river runoff changes).
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