Abstract: The Yangtze River Basin has abundant rainfall, and rainfall is unevenly distributed in space and time, which is prone to flood disasters. It is of great significance to obtain precipitation information accurately and quickly. At present, remote sensing precipitation data has been widely used, but its spatial resolution and data accuracy still cannot meet actual application requirements. Therefore, based on the full consideration of the applicability of TRMM 3B43 data for 2001-2019 in the Yangtze River Basin using a geographically weighted regression model, combining NDVI, EVI, elevation, slope, and aspect, different vegetation index combinations are selected to establish low and high resolution GWR models based on pixels to achieve the downscaling of TRMM data in the Yangtze River basin from 2001 to 2019. Geographical Differential Analysis (GDA) and Geographical Ratio Analysis (GRA) corrections are then applied to the preferred TRMMNDVI data. Finally, accuracy evaluation and result analysis are performed on annual, seasonal, and monthly scales based on the actual measurement data from the meteorological stations. The results of the study showed that, (1) the R², BIAS and RMSE of the downscaled data and the measured data at the site meet the accuracy requirements, while the spatial resolution is improved from 0.25° to 1km, and the accuracy of TRMMNDVI data is higher than the TRMMEVI data. (2) The GDA calibration results are better than the GRA calibration results, and the GDA calibration results shows a higher stability and precise accuracy. Therefore, GDA calibration is more suitable for the calibration of TRMM data in the Yangtze River Basin. (3) The data have high accuracy on annual (0.91−0.986), seasonal (0.704−0.88), and monthly (0.625−0.89) scales with the site measured data R², and its detailed characteristics are better than TRMM data. (4) The downscaling and correction are better in the months with higher precipitation. The TRMM data of downscaling and correction can better reflect the real precipitation information of the Yangtze River Basin, and it provide reliable data support for agricultural production, optimal allocation of water resources, and flood prevention and disaster reduction.

Key words: TRMM3B43, GWR, Scale down, GDA, Yangtze river basin