Direct and indirect application of univariate and multivariate biascorrections on heat-stress indices based on multiple regional-climate-model simulations

Statistical bias correction (BC) is a widely used tool topost-process climate model biases in heat-stress impact studies, which areoften based on the indices calculated from multiple dependent variables.This study compares four BC methods (three univariate and one multivariate)with two correction strategies (direct and indirect) for adjusting twoheat-stress indices with different dependencies on temperature and relativehumidity using multiple regional climate model simulations over SouthKorea. It would be helpful for reducing the ambiguity involved in thepractical application of BC for climate modeling and end-user communities.Our results demonstrate that the multivariate approach can improve thecorrected inter-variable dependence, which benefits the indirect correctionof heat-stress indices depending on the adjustment of individual components,especially those indices relying equally on multiple drivers. On the otherhand, the direct correction of multivariate indices using the quantile deltamapping univariate approach can also produce a comparable performance in thecorrected heat-stress indices. However, our results also indicate thatattention should be paid to the non-stationarity of bias brought by climatesensitivity in the modeled data, which may affect the bias-corrected resultsunsystematically. Careful interpretation of the correction process isrequired for an accurate heat-stress impact assessment.

Automated summary

This study compares four bias correction methods and two correction strategies for adjusting heat-stress indices in regional climate model simulations. The results show that the multivariate approach improves inter-variable dependence and benefits the indirect correction of indices relying equally on multiple drivers. The quantile delta mapping univariate approach also performs well in correcting the heat-stress indices, but attention should be paid to the non-stationarity of bias brought by climate sensitivity.