Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979-2100)

Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979-2014 and future scenarios (SSP245 and SSP585) for 2015-2100 with a horizontal grid spacing of (1.25 degrees x 1.25 degrees) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the individual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth's future climate, atmospheric environment, hydrology, agriculture, wind power, etc.

Automated summary

Dynamical downscaling is crucial for fine-scale weather and climate information, but simulations are often affected by biases in large-scale forcing. A bias-corrected global dataset was developed, showing better quality and practical value for future Earth climate projections.