In the context of global warming, drought disasters are becoming more frequent. In order to comprehensively and finely study the temporal and spatial changes of precipitation in central China from 2010 to 2019, this study combined remote sensing data of water vapor (PWV), enhanced vegetation index (EVI), elevation, slope, and aspect, based on the geographically weighted regression model combined with Kriging Interpolation (GWRK), the GPM IMERG and TRMM 3B43 data from 2010 to 2019 were spatially downscaled, and the data of 53 meteorological stations in the area were used for accuracy evaluation. For the optimized downscaling data, the coefficient of variation method, Theil-Sen Median trend analysis coupled with the Mann-Kendall significance test method and the Hurst index method were used to analyze the temporal and spatial characteristics of precipitation changes and sustainability characteristics in Central China, and future precipitation trends were predicted. Finally, the NPA (Normalized Precipitation Anomaly) and the SPEI12 (standardized precipitation evapotranspiration indices) were constructed to realize the spatio-temporal monitoring of drought in the study area. The research results showed that the details of the precipitation data are enhanced after downscaling, and the overestimation of the original data can be effectively improved. The downscaling data of GPM data was closer to the measured data than the downscaling data of TRMM. In the past 10 years, the temporal and spatial distribution of precipitation in Central China were quite different, with high fluctuations and high fluctuations in precipitation accounting for 69.21% of the entire region. The precipitation changes during the 10 years were dominated by non-significant changes, of which slightly increased areas accounted for 54.88% of the entire region, and slightly decreased areas accounted for 40.41% of the entire region. The future trend of precipitation in Central China was dominated by a continuous increase, mainly distributed in the northern part of the region. Areas with uncertain future trends accounted for 21.63% of the entire region, mainly concentrated in Hunan. There were great differences in the spatial distribution of NPA in Central China. As a whole, it was in a state of drought-free and light-drought disasters. NPA and SPEI₁₂ had a relatively good correlation, and all years had passed the 0.05 significance test, which can be to a certain extent Reflect the drought in Central China. Downscaling data can reflect the temporal and spatial dynamic changes of precipitation in more detail, and is of great significance for accurate monitoring of drought and flood disasters, guiding agricultural production and promoting sustainable economic development.