Abstract: Precipitation data with a high temporal and spatial resolution is of great practical significance to accurately characterize the spatiotemporal changes of regional precipitation in ecological and hydrological processes. However, traditional meteorological station observations cannot meet the high requirements of data acquisition. The purpose of this research was to deal with the time-scale extension in Tropical Rainfall Measuring Mission (TRMM) downscaling, particularly for a higher spatial resolution of TRMM satellite precipitation products under continuous observation and wide coverage. A variable parameter spatial regression model, Geographic Weighted Regression (GWR), was selected for the spatial downscaling of annual and monthly TRMM. Specifically, the parameters were estimated between the dependent and the independent variables at each location via the local weighted least squares method. The study area was taken as the Xiangjiang River in the Dongting Lake water system of the Yangtze River Basin in Hunan Province of China. The specific procedure was as follows. The precipitation data of meteorological stations was first embedded into the TRMM satellite precipitation grid. Then the longitude, latitude, digital elevation models were selected, with the slope, aspect, and normalized difference vegetation index as auxiliary variables. Finally, a TRMM satellite precipitation downscaling model was established using GWR and multiple factors, such as geography, topography, and vegetation. In addition, a variety of scale indexes were used to invert for three products of satellite-ground fusion daily precipitation I, II, and III. The precipitation input data were selected to drive the SWAT distributed hydrological model, further to evaluate the application potential in hydrological simulation. The coefficient of determination, root mean squared error, and average relative error were used to quantitatively evaluate the accuracy of TRMM data before and after downscaling. Moreover, the relative error and Nash-Sutcliffe coefficient of efficiency were also used to quantitatively evaluate SWAT simulation. The results showed that the spatial resolution of TRMM precipitation increased from 0.25° to 0.05°, while the coefficient of determination between the monthly precipitation observed by the meteorological station increased by 0.33 on average, and the root mean square error decreased by 43.30 mm on average, and the average relative deviation decreased by 38.71 percentage points on average after the GWR downscaling, indicating excellent applicability in the TRMM downscaling. Compared with the TRMM daily precipitation, the coefficient of determination between the satellite-ground fusion daily precipitation III product and the meteorological station observation daily precipitation increased by 0.81, the root mean square error decreased by 10.27 mm, and the average relative deviation decreased by 0.11 percentage points, indicating that it was feasible and effective for the meteorological station observation daily precipitation as a proportional index to spread the satellite-ground fusion monthly precipitation. The satellite-ground fusion daily precipitation III product presented the largest Nash efficiency coefficient, the smallest relative error, and the best hydrological simulation effect in the soil and water assessment tool’s daily and monthly runoff. It infers to replace meteorological stations with the TRMM satellite precipitation for hydrological simulation. The finding can provide potential support to high-precision precipitation data acquisition and efficient hydrological simulation in scarce areas of meteorological stations.