Climate projections and renewable energy GFDL-ESM4 SSP126 China

This is an hourly climate projection and renewable generation dataset in China, including 5 climate models and 4 scenarios, corresponding to a Scientific Data paper: Chen, R., Hobbs, B.F., Lu, Z. et al. An hourly climate projection and renewable energy generation dataset for power system modeling in China. Sci Data (2025). https://doi.org/10.1038/s41597-025-06396-5. Due to Zenodo's storage limit (maximum 50GB), all data can not be uploaded to 1 project. Thus, I uploaded all data to 4*5=20 projects according to the model and scenario. This project corresponds to model GFDL-ESM4 and scenario SSP126. If you have any questions, don't hesitate to contact me: [e-mail] Other data can be found in my zenodo homepage or using the following DOIs: Name DOI GFDL-ESM4 SSP126 https://doi.org/10.5281/zenodo.15832921 GFDL-ESM4 SSP245 https://doi.org/10.5281/zenodo.17437774 GFDL-ESM4 SSP370 https://doi.org/10.5281/zenodo.15851824 GFDL-ESM4 SSP585 https://doi.org/10.5281/zenodo.15871433 IPSL-CM6A-LR SSP126 https://doi.org/10.5281/zenodo.15942887 IPSL-CM6A-LR SSP245 https://doi.org/10.5281/zenodo.17444620 IPSL-CM6A-LR SSP370 https://doi.org/10.5281/zenodo.16240944 IPSL-CM6A-LR SSP585 https://doi.org/10.5281/zenodo.16537965 MPI-ESM1-2_HR SSP126 https://doi.org/10.5281/zenodo.16708688 MPI-ESM1-2_HR SSP245 https://doi.org/10.5281/zenodo.17444704 MPI-ESM1-2_HR SSP370 https://doi.org/10.5281/zenodo.16741264 MPI-ESM1-2_HR SSP585 https://doi.org/10.5281/zenodo.16785698 MRI-ESM2-0 SSP126 https://doi.org/10.5281/zenodo.16794804 MRI-ESM2-0 SSP245 https://doi.org/10.5281/zenodo.17444832 MRI-ESM2-0 SSP370 https://doi.org/10.5281/zenodo.16794817 MRI-ESM2-0 SSP585 https://doi.org/10.5281/zenodo.16794824 UKESM1-0-LL SSP126 https://doi.org/10.5281/zenodo.16794897 UKESM1-0-LL SSP245 https://doi.org/10.5281/zenodo.17444964 UKESM1-0-LL SSP370 https://doi.org/10.5281/zenodo.16891177 UKESM1-0-LL SSP585 https://doi.org/10.5281/zenodo.16891179 Some notes: 3 variables are included in VRE.mat: lon_lat_of_VRE_output, CF_wind, and CF_PV. lon_lat_of_VRE_output is a 3848*2 matrix, each row denotes a spatial grid cell. The first column denotes longitude and the second denotes latitude. CF_wind is a 3848*N matrix of the wind power capacity factor converted from meteorological factors, and each row corresponds to a spatial grid cell (with the same row index in lon_lat_of_VRE_output). N is the number of hours, equal to 350640 from 2021 to 2060. Similary, CF_PV is a 3848*N matrix of the PVpower capacity factor converted from meteorological factors. The converting process is presented in the paper.