Evaluating Extreme Storm Events in an Ensemble of High-Resolution Projections

Deeksha Rastogi; Haoran Niu; Shih Chieh Kao; Moetasim Ashfaq

doi:10.1029/2025EF006570

Evaluating Extreme Storm Events in an Ensemble of High-Resolution Projections

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Abstract

This study uses different downscaling techniques and reference observations to investigate the characteristics of extreme storm events over the conterminous United States in historical and a projected future scenario. While previous studies agree on the projected changes in intensity and frequency of precipitation extremes, there is a lack of consensus regarding how their size will change in response to an increase in radiative forcing. Moreover, the influence of different downscaling techniques on their characteristics has not been thoroughly examined. This study employs an ensemble of high-resolution projections derived from six CMIP6 GCMs, using dynamical, statistical and artificial intelligence based downscaling techniques and two reference observations. Overall, we find noticeable differences in the size, average depth, and total precipitation volume of these storms among the climate ensembles in the historical period. Despite these differences in the historical period, we find consistent future changes across various ensembles. We find a robust projected increase in storm size during Winter and Spring but a decrease in size during Summer in the East. Nevertheless, irrespective of changes in their size, extreme storms are projected to intensify across all the ensembles and seasons.

Original language	English
Article number	e2025EF006570
Journal	Earth's Future
Volume	13
Issue number	12
DOIs	https://doi.org/10.1029/2025EF006570
State	Published - Dec 2025

Funding

This study is supported by the US Department of Energy (DOE) Water Power Technologies Office as a part of the SECURE Water Act Section 9505 Assessment and by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. DOE. Support for the climate simulations, data storage, and analysis is provided by the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility. This manuscript has been authored by employees of UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE). Accordingly, the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/downloads/doe-public-access-plan). This study is supported by the US Department of Energy (DOE) Water Power Technologies Office as a part of the SECURE Water Act Section 9505 Assessment and by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT‐Battelle, LLC, for the U. S. DOE. Support for the climate simulations, data storage, and analysis is provided by the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility. This manuscript has been authored by employees of UT‐Battelle, LLC, under contract DEAC05‐00OR22725 with the US Department of Energy (DOE). Accordingly, the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/downloads/doe‐public‐access‐plan ).

Keywords

artificial intelligence
downscaling
extremes

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10.1029/2025EF006570

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title = "Evaluating Extreme Storm Events in an Ensemble of High-Resolution Projections",

abstract = "This study uses different downscaling techniques and reference observations to investigate the characteristics of extreme storm events over the conterminous United States in historical and a projected future scenario. While previous studies agree on the projected changes in intensity and frequency of precipitation extremes, there is a lack of consensus regarding how their size will change in response to an increase in radiative forcing. Moreover, the influence of different downscaling techniques on their characteristics has not been thoroughly examined. This study employs an ensemble of high-resolution projections derived from six CMIP6 GCMs, using dynamical, statistical and artificial intelligence based downscaling techniques and two reference observations. Overall, we find noticeable differences in the size, average depth, and total precipitation volume of these storms among the climate ensembles in the historical period. Despite these differences in the historical period, we find consistent future changes across various ensembles. We find a robust projected increase in storm size during Winter and Spring but a decrease in size during Summer in the East. Nevertheless, irrespective of changes in their size, extreme storms are projected to intensify across all the ensembles and seasons.",

keywords = "artificial intelligence, downscaling, extremes",

author = "Deeksha Rastogi and Haoran Niu and Kao, \{Shih Chieh\} and Moetasim Ashfaq",

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doi = "10.1029/2025EF006570",

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PY - 2025/12

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N2 - This study uses different downscaling techniques and reference observations to investigate the characteristics of extreme storm events over the conterminous United States in historical and a projected future scenario. While previous studies agree on the projected changes in intensity and frequency of precipitation extremes, there is a lack of consensus regarding how their size will change in response to an increase in radiative forcing. Moreover, the influence of different downscaling techniques on their characteristics has not been thoroughly examined. This study employs an ensemble of high-resolution projections derived from six CMIP6 GCMs, using dynamical, statistical and artificial intelligence based downscaling techniques and two reference observations. Overall, we find noticeable differences in the size, average depth, and total precipitation volume of these storms among the climate ensembles in the historical period. Despite these differences in the historical period, we find consistent future changes across various ensembles. We find a robust projected increase in storm size during Winter and Spring but a decrease in size during Summer in the East. Nevertheless, irrespective of changes in their size, extreme storms are projected to intensify across all the ensembles and seasons.

AB - This study uses different downscaling techniques and reference observations to investigate the characteristics of extreme storm events over the conterminous United States in historical and a projected future scenario. While previous studies agree on the projected changes in intensity and frequency of precipitation extremes, there is a lack of consensus regarding how their size will change in response to an increase in radiative forcing. Moreover, the influence of different downscaling techniques on their characteristics has not been thoroughly examined. This study employs an ensemble of high-resolution projections derived from six CMIP6 GCMs, using dynamical, statistical and artificial intelligence based downscaling techniques and two reference observations. Overall, we find noticeable differences in the size, average depth, and total precipitation volume of these storms among the climate ensembles in the historical period. Despite these differences in the historical period, we find consistent future changes across various ensembles. We find a robust projected increase in storm size during Winter and Spring but a decrease in size during Summer in the East. Nevertheless, irrespective of changes in their size, extreme storms are projected to intensify across all the ensembles and seasons.

KW - artificial intelligence

KW - downscaling

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Evaluating Extreme Storm Events in an Ensemble of High-Resolution Projections

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