Direct and Indirect Application of Univariate and Multivariate Bias Corrections on Heat-stress Indices based on Multi-RCM Simulations

Abstract

Abstract. Statistical bias correction (BC) is a widely used tool to post-process climate model biases for heat-stress impact studies, which are often based on indices calculated from multiple dependent variables. This study compares five bias correction methods (four univariate and one multivariate) with two applying strategies (direct and indirect) for correcting two heat-stress indices with different dependencies on temperature and relative humidity, using multiple Regional Climate Model simulations over South Korea. It would be helpful for reducing the ambiguity involved in the practical application of BC for climate modeling as well as end-user communities. Our results demonstrate that the multivariate approach can improve the corrected inter-variable dependence and therefore benefit the indirect correction of heat-stress indices that depend on the adjustment of individual components, especially those relying equally on multiple drivers. On the other hand, the direct correction of multivariate indices using the Quantile Delta Mapping univariate approach can also produce a comparable performance in the corrected heat-stress indices. However, our results also indicate that attention should be paid to the non-stationarity of bias brought by climate sensitivity in the modeled data, which may affect the bias-corrected results unsystematically. Careful interpretation of the correction process is required for an accurate heat-stress impact assessment.