The datasets include standard precipitation index (SPI) at 1-month, 3-month, 6-month, 9-month and 12-month scales over the main terrestrial lands of China from January 1984 to December 2020. The SPI datasets were produced by blending the information from meteorological stations, and precipitation products, as well as topographical and geographical variables based on Gaussian process regression (GPR) models. The meteorological station data are from the China Meteorological Data Service Centre. Five precipitation products are used: (1) CHIRPS Daily: Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final); (3) ERA5-Land Monthly Averaged by Hour of Day - ECMWF Climate Reanalysis; (3) FLDAS: Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System; (4) PERSIANN-CDR: Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record; (5) TerraClimate: Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces. The maps of the difference of the confidence intervals (the upper prediction limit minus the lower prediction limit) at a significance level of 95% are also provided to show the spatial uncertainty of every single SPI map. The drought events were counted during 1984-2020 at annual and seasonal scales. The variables related to the drought events are presented in “Drought_Event.zip”. Reference: He, Q., Wang, M., Liu, K., Li, B., & Jiang, Z. (2023). Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning. Weather and Climate Extremes, 40, 100567.