Implications of statistical bias adjustment for uncertainties in regional model projections

Regional climate models, due to their systematic biases, are not usable for impact assessment and policy-relevant applications. It is common to post-process the regional model outputs with appropriate bias correction methodologies to provide reliable climate change information. We apply a distribution-based, trend-preserving quantile mapping procedure to bias correct the projections of daily precipitation and temperature from an ensemble of 5 RCMs driven by 5 GCMs, each at a resolution of 0.11°, chosen from the EURO-CORDEX initiative. The gridded observations from the German Weather Service, DWD-HYRAS, has been used as a reference for the bias correction. The impact of the bias correction is found to be more pronounced on precipitation than on temperature, as the precipitation biases are larger. The models are wetter and underestimate (overestimate) the daily maximum (minimum) temperature. The correction method eliminates large parts of these biases and maps the distributions of both the variables well with that of observations. The bias adjustment also leads to the narrowing down of the uncertainties in the projected changes of both the variables. The decomposition of total variance into model uncertainty and internal variability suggests that the bias correction acts mostly on the former component. The internal variability component does not seem, however, to undergo considerable changes following the bias correction. Due to the reduction of the uncertainty, we find a slight improvement in the signal-to-noise ratio in the projections.