Runtime bias correction of regional climate model driving data and its continental-scale impacts

Abstract The application of a new approach to bias-correct Earth System Model (ESM) driving data for regional Climate Model (RCM) downscaling is presented. The approach employs a novel Empirical Runtime Bias Correction (ERBC) of the ESM, designed to self-consistently reduce climatological biases in the driving data. The impact of such ESM bias reduction on RCM downscaling is evaluated through an experimental protocol where a single ESM and its ERBC counterpart drive two different RCMs. A continental-scale analysis of these results in a North American regional domain over the historical period indicates that the impact of global model biases on RCM downscaling products can be mitigated significantly by employing ERBC driving data. A similar series of ESM downscaling simulations is conducted for future projections of climate change following the Coupled Model Intercomparison Project phase 6 SSP3 $$-$$ 7.0 scenario of anthropogenic forcings. Unlike diagnostic bias corrections applied to model output, ERBCs have the potential to improve climate-change circulation responses in the ESM, thereby improving driving data and reducing uncertainty in RCM projections. This reduction would be reflected in a narrower spread of responses within a multi-model ensemble of ESMs and RCMs. Since this study involves only one ESM, we investigate the necessary but not sufficient condition for uncertainty reduction: that ERBCs can alter climate change responses compared to their uncorrected counterparts. The results show that ERBCs induce statistically significant changes in the climate-change circulation responses in both the global and regional models.