Weather and Climate Dynamics
Weather and Climate Dynamics
WCD
Articles | Volume 6, issue 4
Articles | Volume 6, issue 4
https://doi.org/10.5194/wcd-6-1147-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-6-1147-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 4
Research article
 | Highlight paper
 | 
21 Oct 2025
Research article | Highlight paper |  | 21 Oct 2025

Estimating return periods for extreme events in climate models through Ensemble Boosting

Luna Bloin-Wibe, Robin Noyelle, Vincent Humphrey, Urs Beyerle, Reto Knutti, and Erich Fischer

Data sets

Estimating return periods for extreme events in climate models through Ensemble Boosting L. Bloin-Wibe et al. https://doi.org/10.3929/ethz-b-000720049

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Ubiquity of human-induced changes in climate variability (https://www.cesm.ucar.edu/community-projects/lens2/data-sets) K. B. Rodgers et al. https://doi.org/10.5194/esd-12-1393-2021

Model code and software

Boosting_estimator Luna Bloin-Wibe, Robin Noyelle, and Vincent Humphrey https://github.com/luna-bloin/Boosting_estimator

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Executive editor
This study introduces an original methodology combining ensemble boosting and conditional probability theory to estimate return periods of rare climate extremes without relying on long climate simulations. The method is rigorously developed, validated on a red-noise process, and applied to CESM2 simulations, including an analysis of the 2021 Pacific Northwest heatwave. Importantly, this framework enables linking probability estimates to specific climate storylines, allowing an assessment of the odds that an extreme event like the one examined will occur in the future.
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Short summary
Weather extremes have become more frequent due to climate change. It is therefore crucial to understand them, but since they are rarer than average weather, they are challenging to study. Ensemble Boosting (EB) is a tool that generates extreme climate model events efficiently, but without directly estimating their probability. Here, we present a method to recover these probabilities for a global climate model. EB can thus now be used to find extremes with meaningful statistical information.
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