Deep learning downscaled CMIP6 high-resolution (0.1°) daily near surface meteorological datasets over East Asia (ensemble mean)

Downscaling is a method to obtain high-resolution meteorological data. UNet, a great potential deep learning technology, is gaining popularity in downscaling. Based on 19 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the Multi-Source Weather (MSWX) dataset, we use bias correction and UNet downscaling to develop an East Asian dataset, called Climate Change for East Asia with Bias Corrected UNet Dataset (CLIMEA-BCUD). CLIMEA-BCUD provides nine meteorological variables including 2-m air temperature, 2-m daily maximum air temperature, 2-m daily minimum air temperature, precipitation, 10-m wind speed, 2-m relative humidity, 2-m specific humidity, downward shortwave radiation and downward longwave radiation with 0.1° horizontal resolution at daily intervals over the historical period of 1950-2014 and three future scenarios (SSP126, SSP245 and SSP585) of 2015-2100. The validations against MSWX shows that CLIMEA-BCUD shows good performance in terms of climatological and can simulates seasonal cycle and future change well. CLIMEA-BCUD can prompt the development of deep learning in climate research and be used for climate change, hydrology, agriculture, energy, etc.Here we provide the multi-model ensemble of CLIMEA-BCUD.