1 km monthly precipitation and temperatures dataset for China from 1952 to 2019 based on new baseline climatology surfaces

Long-term climate data and high-quality baseline climatology surface with high resolution are essential to investigate climate change and its effect on hydrological processes and ecosystem functioning. However, large uncertainties remain in the climate products in China owing to lacking of high-density distribution network of weather stations. Here, the thin plate spline (TPS) algorithm was used to produce new baseline climatology surfaces (ChinaClim_baseline) using >2000 freely available weather stations. Then, climatologically aided interpolation (CAI) was employed to generate a 1 km monthly precipitation and temperatures dataset for China during 1952-2019 (ChinaClim_time-series) via superimposing ChinaClim_baseline and monthly anomaly surface. Our finding showed that ChinaClim_baseline performed exceptionally well in four climatic regions, with RMSEs for precipitation and temperature element estimation of 1.276-28.439 mm and 0.310-2.040 degrees C, respectively. The correlations among ChinaClim_baseline and WorldClim2 and CHELSA were high, but there were clearly spatial differences. For ChinaClim_time-series, precipitation and temperature elements had average RMSEs between 7.502- 52.307 mm, and 0.461-0.939 degrees C for all months, respectively. In comparison to Peng's climatic surface and CHELSAcruts, R-2 increased by similar to 7 %, RMSE and MAE dropped by similar to 17 % for precipitation; R-2 hardly increased, while RMSE and MAE decreased by similar to 50 % for temperature elements. Our findings indicated that ChinaClim_baseline improved the accuracy of time-series climatic elements estimation obviously, and the satellite-driven data can greatly improve the accuracy of time-series precipitation estimation, but not the accuracy of time-series temperature estimation. Overall, ChinaClim_baseline as an excellent baseline climatology surface can be used for obtaining high-quality and long-term climate datasets from past to future. Meantime, ChinaClim_time-series of 1 km spatial resolution is appropriate for investigating the spatial-temporal climate changes and their impacts on eco-environmental systems in China.