A 1 km monthly dataset of historical and future climate changes over China

This dataset provides 30-year averaged climate data for both historical and future periods, with a spatial resolution of 0.01° × 0.01°. Historical data (1991–2020) are based on the China Surface Climate Standard Dataset and were interpolated using ANUSPLIN software. Future climate data are derived from CMIP6 simulations, bias-corrected using the Delta downscaling method. The dataset includes 10 models (9 Global Climate Models, namely, GCMs, and 1 ensemble model), 3 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and 3 future periods (2021–2040, 2041–2070, 2071–2100). For each period (or scenario), 28 climate variables are provided, including: 5 monthly basic climate variables (mean temperature, maximum temperature, minimum temperature, precipitation, and percentage of sunshine), and 23 bioclimatic variables based on the basic variables (for details, see the dataset documentation file).The data quality was strictly evaluated. The ANUSPLIN interpolated historical data showed a strong correlation with observations (all correlation coefficients above 0.91). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The bias correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%–58.63%. This dataset aims to provide high-resolution, bias-corrected long-term historical and future climate data for climate and ecological research. All computations were performed using R, and the corresponding code can be found in the dataset folder: “Code”.All data are provided in GeoTIFF (.tif) format, where each file for the basic climate variables contains 12 bands, representing monthly data in ascending order (e.g., Band 1 corresponds to January). To facilitate data storage, all files are provided in compressed archives, following a consistent naming convention:(1) Historical data: China_Variable_1km_1991–2020.tifWhere, Variable represents the abbreviation of the 28 climate variables.Example: China_pr_1km_1991–2020.tif.(2) Future data: China_Variable_Model_VariantLabel_1km_StartYear-EndYear_Scenario.tifWhere, Variable is the 28 climate variables; Model is the GCM name; VariantLabel is r1i1p1f1 in this study; StartYear-EndYear is the future period; Scenario is the SSP climate scenarioExample: China_tasmin_MRI-ESM2-0_r1i1p1f1_1km_2071–2100_SSP585.tif.