Disentangling the effect of bias adjustment on climate change projections of heat stress in Southeastern South America

Climate impact assessment requires more detailed, sector-specific climate information, especially when impacts depend on crossing specific thresholds, such as heat-stress conditions. Regional climate models (RCMs) can provide such high-resolution climate projections, but systematic biases hinder their direct use. Therefore, bias adjustment (BA) methods are commonly applied in impact studies devoted to heat-stress, which, besides, is a multivariate hazard. Selecting an appropriate BA method for multivariable indices remains challenging due to the need to preserve inter-variable dependence structures and the climate change signal.

This study examines multiple BA methods to generate regional climate projections of two multivariable heat-stress indices—wet-bulb temperature (wbt) and a simplified version of the wet-bulb globe temperature (swbgt)—over southeastern South America (SESA). Both indices rely on temperature and humidity but differ in their sensitivity to these input climate variables. For this assessment, five BA methods were analysed, including trend-preserving and non-trend-preserving techniques as well as univariate and multivariate approaches. 

CORDEX and CORDEX-CORE RCM simulations available for SESA driven by three different global climate models were considered, and the MSWX dataset was used as reference. To adjust the indices, an indirect approach was adopted, with the individual input climate variables adjusted prior to index calculation. All methods were trained on austral summer days from the historical period and then applied to RCP8.5 future simulations. Future changes were assessed for the mean and maximum summer values, as well as for two frequency-based metrics using heat-stress thresholds in order to examine the contribution of the RCM and BA method to the overall uncertainty.

Climate change projections obtained from trend-preserving and non-trend-preserving methods considerably differed in magnitude and spatial distributions, with non–trend-preserving approaches typically underestimating the RCMs raw signal, clearly for the mean values. Multivariate methods enhanced the representation of heat-stress indices during training, better capturing the correlation between temperature and humidity, although no added value was identified in the projected delta changes.

Large uncertainties within RCMs raw outputs and BA methods were found in the magnitude of the change signal for the climate input variables, especially for humidity, which were considerably reduced after computing the indices. In particular, the differing sensitivities of the indices to temperature and humidity were highlighted: wbt closely reflected regions with large humidity-related uncertainties, whereas swbgt aligned more closely with the spatial patterns of temperature uncertainties.

This study provides valuable information on the use of BA methods in multivariable impact studies in SESA—a region where fine-scale climate projections remain limited—and underscores the importance of carefully evaluating BA methods prior to climate-impact applications, particularly in a multivariable, climate-change context.