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.