The 30-year average climate dataset (1981-2010) comes from two sources, namely the China Meteorological Data Service Center (CMD: http://data.cma.cn ）2160 meteorological stations and 25 meteorological stations of the Central Weather Bureau (www.cwb. gov.tw). The monthly ground observation dataset of 756 meteorological stations from 1952 to 2019 is from the China Meteorological Administration http://data.cma.cn .

    The TRMM3B43 product was used in the study, with a spatial resolution of 0.25 ° and a latitude range of 50 ° S to 50 ° N. follow https://mirador.gsfc.nasa.gov I downloaded the monthly data of TRMM3B43 7th edition in NetCDF format.

    The CAI method was used to generate monthly precipitation data (ChinaClim time series) for China from 1952 to 2019. The precipitation ratio was calculated by the ratio and difference between the original time series of the meteorological station and the 30-year normal value. Combining the longitude, latitude, altitude, distance to the nearest coast, satellite driven anomaly (ratio), CRU anomaly (ratio), and 30-year normal value of each meteorological station, based on their geographical coordinates, the TPS model was applied to generate a monthly precipitation anomaly plane from 1952.01 to December 2019. For the monthly anomaly/ratio from 1952 to 2019, different variable combinations (longitude, latitude, altitude, distance to the nearest coast, CRU anomaly (ratio), and 30-year normal value) were used to construct seven model formulas (Table S2), and the optimal model was selected by the minimum RMSE value of the multi-year (1952-2019) average to fit the precipitation anomaly plane from 1952 to 1997. In the remaining time, we constructed two model formulas based on the optimal model in step (3). These two models add satellite data (TRMM ratio and LST anomaly) as independent spline variables or linear covariates. The ChinaClim_time sequence is generated by superimposing (multiplying) the monthly anomaly (ratio) surface from 1952.01-209.12 and the ChinaClim_baseline.