Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 8
Articles | Volume 25, issue 8
https://doi.org/10.5194/nhess-25-2863-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2863-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 8
Research article
 | 
26 Aug 2025
Research article |  | 26 Aug 2025

Reask UTC: a machine learning modeling framework to generate climate-connected tropical cyclone event sets globally

Thomas Loridan and Nicolas Bruneau

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Short summary
Tropical cyclone (TC) risk models have been used by the insurance industry to quantify occurrence and severity risk since the early 1990s. To date, these models have mostly been built from backward-looking statistics and portray risk under a static view of the climate. Here we introduce a novel approach, based on machine learning, that allows sampling of climate variability when assessing TC risk globally. This is of particular importance when computing forward-looking views of TC risk.
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