High-resolution climate data are important for understanding the impacts of climate change on multiple sectors worldwide. In this study, based on the latest released meteorological records during 1991-2020 and the recently updated general circulation models (GCMs), we established a 30-year averaged 0.01° (≈1 km) dataset of 5 basic climate variables and 23 bioclimatic variables, using ANUSPLIN software, delta correction (DC) downscaling, and cubic spline resampling method. Each variable contained monthly gridded historical data during 1991-2020 and bias-corrected future data over three periods (2021-2040, 2041-2070, 2071-2100), three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and 10 GCMs (including an ensemble model). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The DC correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%-58.63%. This new dataset therefore demonstrates reliable data quality, and further provides high-resolution and bias-corrected long-term averaged historical and future climate data across China for ecological and climate impact studies.