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. Along with the higher demand for bias-corrected data for climate impactstudies, the number of available data sets has largely increased inrecent years. For instance, the Inter-Sectoral Impact Model IntercomparisonProject (ISIMIP) constitutes a framework for consistently projecting theimpacts of climate change across affected sectors and spatial scales. Thesedata are very attractive for any impact application since they offerworldwide bias-corrected data based on global climate models (GCMs).In a complementary way, the CORDEX initiative has incorporated experiments based onregionally downscaled bias-corrected data by means of debiasing and quantilemapping (QM) methods. In light of this situation, it is challenging todistil the most accurate and useful information for climate services, but atthe same time it creates a perfect framework for intercomparison andsensitivity analyses. In the present study, the trend-preserving ISIMIP method and empirical QMare applied to climate model simulations that were carried out at differentspatial resolutions (CMIP5 GCM and EURO-CORDEX regional climate models(RCMs), at approximately 150, 50 and 12 km horizontal resolution) in order to assess the role of downscaling and bias correctionin a multivariate framework. The analysis is carried out for the wet-bulbglobe temperature (WBGT), a heat stress index that is commonly used in thecontext of working people and labour productivity. WBGT for shadedconditions depends on air temperature and dew-point temperature, which inthis work are individually bias corrected prior to the index calculation.Our results show that the added value of RCMs with respect to the drivingGCM is limited after bias correction. The two bias correction methods areable to adjust the central part of the WBGT distribution, but some addedvalue of QM is found in WBGT percentiles and in the inter-variablerelationships. The evaluation in present climate of such multivariateindices should be performed with caution since biases in the individualvariables might compensate, thus leading to better performance for the wrongreason. Climate change projections of WBGT reveal a larger increase insummer mean heat stress for the GCM than for the RCMs, related to thewell-known reduced summer warming of the EURO-CORDEX RCMs. These differencesare lowered after QM, since this bias correction method modifies the changesignals and brings the results for the GCM and RCMs closer to each other. Wealso highlight the need for large ensembles of simulations to assess thefeasibility of the derived projections.

Climate projections of a multivariate heat stress index: the role of downscaling and bias correction