Abstract: A high-resolution SPEI dataset development method based on a random forest regression model is provided. In the method, meteorological station data, GPM remote sensing precipitation data, MODIS land surface temperature data, ERA5-Land shortwave radiation data and SRTM digital elevation model data are combined; and a spatial pattern of SPEI index at different time scales of a target area is predicted by constructing a spatiotemporal relationship between the SPEI index and the precipitation, land surface temperature, shortwave radiation and elevation data. The method fully utilizes advantages that the random forest is high in precision and avoids overfitting in model prediction, and inputs station data and remote sensing and reanalysis data simultaneously into the model for training, which can solve problems of mismatch of an existing SPEI dataset with the station data and low spatial resolution, and the spatial resolution of SPEI dataset is effectively improved.