Articles | Volume 14, issue 2
https://doi.org/10.5194/esd-14-507-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-507-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations
Department of Civil and Environmental Engineering, The Hong Kong
University of Science and Technology, Hong Kong SAR, China
Department of Civil and Environmental Engineering, The Hong Kong
University of Science and Technology, Hong Kong SAR, China
Division of Environment and Sustainability, The Hong Kong University
of Science and Technology, Hong Kong SAR, China
Seung-Ki Min
Division of Environmental Science and Engineering,
Pohang University of Science and Technology, Pohang, South Korea
Institute for Convergence Research and Education in Advanced
Technology, Yonsei University, Incheon, South Korea
Yeon-Hee Kim
Division of Environmental Science and Engineering,
Pohang University of Science and Technology, Pohang, South Korea
Dong-Hyun Cha
Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
Seok-Woo Shin
Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
Joong-Bae Ahn
Department of Atmospheric Sciences, Pusan National University, Busan, South Korea
Eun-Chul Chang
Department of Atmospheric Sciences, Kongju National University, Gongju, South Korea
Young-Hwa Byun
Climate Change Research Team, National Institute of Meteorological Science, Seogwipo, South Korea
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- Projected increase in global compound agricultural drought and hot events under climate change W. Shi et al. https://doi.org/10.1016/j.gloplacha.2025.104962
- Projected avoidance of heat risk in the Arabian Peninsula under carbon neutrality scenarios S. Ullah et al. https://doi.org/10.1016/j.indic.2025.100999
- Spatiotemporal extension of extreme heat stress over East Asia under shared socioeconomic pathways Y. Kim et al. https://doi.org/10.1016/j.wace.2023.100618
- Development of localized climate projections for Vietnam’s Mekong Delta using univariate and multivariate bias corrections of CMIP6 simulations D. Dong Phuong et al. https://doi.org/10.1007/s00382-026-08347-1
- Substantial increases in the likelihood of extreme fire weather events for fire-prone ecosystems in Australia R. McGloin et al. https://doi.org/10.1038/s44304-026-00193-9
- Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen https://doi.org/10.1007/s11430-023-1201-y
- Characterization of temperature and humidity effects on extreme heat stress under global warming and urban growth in the Pearl and Yangtze River Deltas of China Z. Zhou et al. https://doi.org/10.1016/j.wace.2024.100659
- Amplification of the discrepancy between simplified and physics-based wet-bulb globe temperatures in a warmer climate L. Qiu et al. https://doi.org/10.1016/j.wace.2024.100677
- Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin C. Thakur et al. https://doi.org/10.1016/j.ejrh.2025.102443
- Gaussian random field-based downscaling for drought projections: linking CMIP6 SSPs to local hydrology in a subtropical river basin S. He et al. https://doi.org/10.1016/j.jhydrol.2026.135292
10 citations as recorded by crossref.
- Projected increase in global compound agricultural drought and hot events under climate change W. Shi et al. https://doi.org/10.1016/j.gloplacha.2025.104962
- Projected avoidance of heat risk in the Arabian Peninsula under carbon neutrality scenarios S. Ullah et al. https://doi.org/10.1016/j.indic.2025.100999
- Spatiotemporal extension of extreme heat stress over East Asia under shared socioeconomic pathways Y. Kim et al. https://doi.org/10.1016/j.wace.2023.100618
- Development of localized climate projections for Vietnam’s Mekong Delta using univariate and multivariate bias corrections of CMIP6 simulations D. Dong Phuong et al. https://doi.org/10.1007/s00382-026-08347-1
- Substantial increases in the likelihood of extreme fire weather events for fire-prone ecosystems in Australia R. McGloin et al. https://doi.org/10.1038/s44304-026-00193-9
- Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen https://doi.org/10.1007/s11430-023-1201-y
- Characterization of temperature and humidity effects on extreme heat stress under global warming and urban growth in the Pearl and Yangtze River Deltas of China Z. Zhou et al. https://doi.org/10.1016/j.wace.2024.100659
- Amplification of the discrepancy between simplified and physics-based wet-bulb globe temperatures in a warmer climate L. Qiu et al. https://doi.org/10.1016/j.wace.2024.100677
- Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin C. Thakur et al. https://doi.org/10.1016/j.ejrh.2025.102443
- Gaussian random field-based downscaling for drought projections: linking CMIP6 SSPs to local hydrology in a subtropical river basin S. He et al. https://doi.org/10.1016/j.jhydrol.2026.135292
Saved (final revised paper)
Latest update: 01 Sep 2026
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.
This study evaluates four bias correction methods (three univariate and one multivariate) for...
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