Investigation of added values in multi-model and multi-variable bias adjustments for heat stress assessment

Regional climate models (RCMs) are widely used to dynamically downscale the general circulation models (GCMs). Downscaled products can provide a clearer understanding of atmospheric processes compared to the parent models. However, several uncertainties are associated with downscaling, such as structural differences in climate models and biases in GCMs and RCMs. Post-processing methods such as univariate bias correction have been widely used to reduce the bias in the individual variable. However, these methods are applied to variables independently without considering the inter-variable dependence. In compound events such as heat stress, multiple drivers, surface air temperature (SAT), and relative humidity (RH) play crucial roles. Therefore, a multi-variable bias adjustment is necessary to retain the interdependence between the drivers for reliable information on heat stress. The present study focuses on a Multi-variable Bias Adjustment (MBA) method adapted from a topographical adjustment of SAT and RH and its impact on added values in a multi-model ensemble. We investigated added values and biases before and after adjusting the variables. There are gains and losses throughout the process of bias adjustment. Some added values show pseudo nature over some regions after the bias adjustment. Overall, the bias adjustment shows improvement in reducing bias over low-altitude urban areas, encouraging its application to assess heat stress.