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 Land Surface Temperature (LST) is compiled by the Medium Resolution Imaging Spectroradiometer (MODIS). The day night LST average values from 2001 to 2019 were extracted from MOD11A2 images with a resolution of 1 kilometer, and averaged by month and year.

    The CAI method was used to generate the ChinaClim time series temperature surface data from 1952 to 2019 in China. The temperature anomaly time series was calculated by the ratio and difference between the original time series of meteorological stations and the 30-year normal values. Based on the longitude, latitude, altitude, distance to the nearest coast, satellite driven anomaly (ratio), CRU anomaly (ratio), and 30-year normal values of each meteorological station, the TPS model was applied to generate a temperature anomaly plane from January 1952 to December 2019, using a method similar to the ChinaClim baseline. For the monthly anomaly/ratio from 1952 to 2019, seven model formulas were constructed using different combinations of variables (longitude, latitude, altitude, distance to the nearest coast, CRU anomaly (ratio), and 30-year normal value), and the optimal model was selected based on the minimum RMSE value of the multi-year (1952-2019) average to fit the temperature anomaly plane from 11952-2000. The ChinaClim_time sequence is generated by superimposing (multiplying) the monthly anomaly (ratio) surface from 1952.01-209.12 and the ChinaClim_baseline.

    The research results indicate that in ChinaClim time series, the average root mean square error of temperature elements for each month is 0.461-0.939 ℃. Compared with the climate surface of Peng Dehuai and CHELSAcruts, the R2 of temperature elements has hardly increased, while RMSE and MAE have decreased by about 50%. The research results indicate that ChinaClim baseline has significantly improved the estimation accuracy of time series climate elements. Satellite driven data can significantly improve the estimation accuracy of time series precipitation, but cannot improve the estimation accuracy of time series temperature.