Downscaled TRMM satellite precipitation data from 2001 to 2019 in the Sichuan Province, China

Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, the existing methods often fail to adequately consider the spatial heterogeneity and complex nonlinearity between precipitation and surface parameters, resulting in poor downscaling performance and inaccurate expression of regional details. In this study, we propose a precipitation downscaling model based on Geographically Weighted Neural Network Regression (GNNWR), which integrates Normalized Difference Vegetation Index, Digital Elevation Model, Land Surface Temperature, and slope data to address spatial heterogeneity and complex nonlinearity. We explore the spatiotemporal trends of precipitation in the Sichuan region over the past two decades. The results show that the GNNWR model outperforms common methods in downscaling precipitation for the four distinct seasons, achieving a maximum R² of 0.972 and a minimum RMSE of 3.551 mm. Overall, precipitation in Sichuan Province exhibits a significant increasing trend from 2001 to 2019, with a spatial distribution pattern of low in the northwest and high in the southeast. The GNNWR downscaled results exhibit the strongest correlation with observed data and provide a more accurate representation of precipitation spatial patterns.