Correcting biases in regional climate model boundary variables for improved simulation of high-impact compound events

Abstract

Although climate models have been used to assess compound events, the combination of multiple hazards or drivers poses uncertainties because of the systemic biases present. Here, we investigate multivariate bias correction for correcting systemic bias in the boundaries that form the inputs of regional climate models (RCMs). This improves the representation of physical relationships among variables, essential for accurate characterization of compound events. We address four types of compound events that result from eight different hazards. The results show that while the RCM simulations presented here exhibit similar performance for some event types, the multivariate bias correction broadly improves the RCM representation of compound events compared to no correction or univariate correction, particularly for coincident high temperature and high precipitation. The RCM with uncorrected boundaries tends to produce a negative bias in the return period of these events, suggesting a tendency to over-simulate compound events with respect to observed events.