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. Here, we construct a set of bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a nonlinear trend from the mean of 18 CMIP6 models. The dataset spans the historical period of 1979–2014 and future scenarios (SSP245 and SSP585) of 2015–2100 with a horizontal resolution of 1.25° × 1.25° and 6-hourly intervals. Our evaluation suggests that the bias-corrected data shows clearly better quality than individual CMIP6 models evaluated in terms of climatological mean, interannual variance and extreme events. The presented dataset will be useful for the dynamical downscaling projections of future climate, atmospheric environment, hydrology, agriculture, wind power, etc.