In order to improve the spatial resolution of satellite precipitation data such as Integrated Multi-satellite Retrievals for Global Precipitation Measurement ( GPM IMERG) , the Beibu Gulf Economic Zone was taken as the study area to explore the optimal downscaling schemes. Using the global precipitation measurement( GPM) satellite precipitation data from 2001 to 2020 as the data source, a geographically weighted regression model ( GWR ) was constructed combining normalized difference vegetation index (NDVI) , enhanced vegetation index( EVI) , digital elevation model( DEM) and slope data. NDVI and EVI were used as explanatory variables to downscale the GPM data in the Beibu Gulf Economic Zone, and the impact of the vegetation index with a lag period of 0 ~ 3 months on the downscaling results was further discussed. Then, the accuracy was verified based on the 43 meteorological stations in the study area. The results show that GPM data and measured data from meteorological stations show good consistency and applicability in time and space, and the determination coefficient of each station is between 0. 65 and 0. 93. On the premise of ensuring the data accuracy, the spatial resolution of the downscale data has been greatly improved ( from 0. 1° to 1 km) , and the EVI downscale GPM data accuracy is better than the NDVI downscale results. When considering time lag effects in translation, using the EVI with a lag of 1 ~ 2 months as the explanatory variable, the downscaled GPM data accuracy is optimal, and the downscaled results are better for dry years than for wet years. In the Beibu Gulf Economic Zone, the EVI is more suitable than the NDVI as a downscaled factor for GPM data, using a lag of 1 ~ 2 months.