Articles | Volume 12, issue 8
Geosci. Model Dev., 12, 3419–3438, 2019
Geosci. Model Dev., 12, 3419–3438, 2019

Model evaluation paper 05 Aug 2019

Model evaluation paper | 05 Aug 2019

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

Ana Casanueva et al.

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Cited articles

Bedia, J., Gutiérrez, J. M., Herrera, S., Iturbide, M., and Manzanas, R.: downscaleR: An R package for bias correction and statistical downscaling. R package version 2.0.4, available at: (last access: 2 August 2019), 2017. 
Bernard, T. E. and Pourmoghani, M.: Prediction of Workplace Wet Bulb Global Temperature, Applied Occupational and Environmental Hygiene, 14, 126–134, 1999. 
Casanueva, A.: HeatStress, Zenodo,, 2019a. 
Casanueva, A.: plotFun, Zenodo,, 2019b. 
Casanueva, A., Kotlarski, S., Herrera, S., Fernández, J., Gutiérrez, J. M., Boberg, F., Colette, A., Christensen, O. B., Goergen, K., Jacob, D., Keuler, K., Nikulin, G., Teichmann, C., and Vautard, R.: Daily precipitation statistics in a EURO-CORDEX RCM ensemble: added value of raw and bias-corrected high-resolution simulations, Clim. Dynam., 47, 719–737, 2016. 
Short summary
Given the large number of available data sets and products currently produced for climate impact studies, it is challenging to distil the most accurate and useful information for climate services. This work presents a comparison of methods widely used to generate climate projections, from different sources and at different spatial resolutions, in order to assess the role of downscaling and statistical post-processing (bias correction).