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

Abstract

Statistical bias correction (BC) is a widely used tool to post-process climate model biases in heat-stress impact studies, which are often 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 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 and end-user communities. Our results demonstrate that the multivariate approach can improve the corrected inter-variable dependence, which benefits the indirect correction of heat-stress indices depending on the adjustment of individual components, especially those indices 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.