Accurate precipitation data with high spatial resolution is important for hydrological, meteorological, and environmental applications. Tropical Rainfall Measuring Mission (TRMM) provides multiple precipitation products with a spatial resolution of 0.25°, and the resolution is too coarse to meet the requirements for hydrological study on local regions and watersheds scale. In this paper, geographically weighted regression (GWR) and multiple linear regression models were used for TRMM data downscaling. GWR is suitable to explore and describe the complex relationship between precipitation and other environmental variables. Global regression analysis applied uniform model parameters over space, which may provide a poor description of the local relationship and could result in important details being missed. By adopting GWR, the strength of the relationship among TRMM, NDVI, and DEM increased markedly, with a minimum of 91% of the variation in TRMM values explained by those in NDVI and DEM (R 2  = 0.911). Comparing the downscaled precipitation with the observed precipitation data, the results show that GWR has an increased R 2  of 0.758 and reduced RMSE of 68.4mm, comparing with the global regression method with R 2  of 0.601 and RMSE of 88.4mm
['Qisheng He', 'Tao Yang', 'Baozhu Liu', 'Si Zhou']
Study on the satellite-based precipitation downscaling algorithm in Tianshan mountain