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This study conducts the spatial interpolation analysis of the temperature and precipitation of 38 years across the country, and selects the optimal model to generate a 1 km grid dataset to provide support for the researches on vegetation distribution, climate change and environmental ecology in mainland China. Based on the daily temperature data and precipitation data from 839 meteorological stations of the National Meteorological Center, using longitude, latitude and altitude as the three variables interpolated by the ANUSPLIN software, the square root pretreatment of precipitation is carried out, and with the thin disk smooth spline method of the thirdorder spline, the 1 km grid interpolation dataset of monthly average temperature and monthly cumulative precipitation in mainland China from 1980 to 2017 is obtained. The square root of Generalized CrossValidation and RootMeanSquare Error (RMSE) of this dataset have the characteristics of annual periodicity and obvious seasonal variation. The frequency distribution of the Mean Bias Error (MBE) of each station is close to the normal distribution, and the spatial distribution of the Mean Absolute Error (MAE) is also consistent with the changing characteristics of the climate of mainland China. The dataset is relatively new in accuracy and time series and is available for public download, which can provide information support for the study of the national terrestrial ecosystem.