Articles | Volume 14, issue 2
Research article
26 Apr 2023
Research article |  | 26 Apr 2023

Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations

Liying Qiu, Eun-Soon Im, Seung-Ki Min, Yeon-Hee Kim, Dong-Hyun Cha, Seok-Woo Shin, Joong-Bae Ahn, Eun-Chul Chang, and Young-Hwa Byun

Data sets

Near-surface temperature and relative humidity data from the CORDEX-East domain downscaling product L. Qiu, E.-S. Im, S.-K. Min, Y.-H. Kim, D.-H. Cha, S.-W. Shin, J.-B. Ahn, E.-C. Chang, and Y.-H. Byun

ERA5 hourly data on single levels from 1940 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut

Model code and software

R package "MBC": Multivariate Bias Correction of Climate Model Outputs A. J. Cannon

R package "qmap": Statistical Transformations for Post-Processing Climate Model Output L. Gudmundsson

Short summary
This study evaluates four bias correction methods (three univariate and one multivariate) for correcting multivariate heat-stress indices. We show that the multivariate method can benefit the indirect correction that first adjusts individual components before index calculation, and its advantage is more evident for indices relying equally on multiple drivers. Meanwhile, the direct correction of heat-stress indices by the univariate quantile delta mapping approach also has comparable performance.
Final-revised paper