[Objective] The spatial downscaling and error correction of tropical rainfall measuring mission (TRMM) data at different time scales were researched in order to provide references for flood disaster monitoring in Central China. [Methods] This article mainly used geographically weighted regression (GWR) model to achieve spatial downscaling of TRMM data from 2001 to 2019 with the help of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI), and compared and analyzed the annual, seasonal and monthly downscaled data through meteorological station data. Then combined with the geographic difference analysis (GDA) and geographic ratio analysis (GRA), the downscaling results of the year, quarter and month were corrected for error, and the data before and after the correction were compared and analyzed. [Results] ① The coefficient of determination (R²) of TRMM data and meteorological station data in year (0.630), season (0.710～0.865) and month (0.637～0.875) all showed that the TRMM data had better applicability in Central China. ② The spatial resolution of the TRMM data was downscaled from 0.25° to 1 km through the GWR model, and TRMM_EVI data had better accuracy than TRMM_NDVI data, indicating that TRMM data in Central China had a closer relationship with EVI than NDVI. ③ GDA and GRA corrections were performed on the optimized TRMM_EVI data. The GDA correction results were better than the GRA corrections, and the correction effect was better in months with more precipitation. [Conclusion] In Central China, EVI is more suitable for TRMM data downscaling research than NDVI, and downscaling data using GDA correction is more effective than GRA correction.