Understanding the Intricacies of Model Biases in Storm-Related Extreme Precipitation Using CMIP6

Xiaorui Li; Bo Liu; Bin Wang; Guoyu Ren; Cristian Martinez-Villalobos; Meiyu Chang; Zhongshi Zhang

doi:10.1029/2025GL115365

Understanding the Intricacies of Model Biases in Storm-Related Extreme Precipitation Using CMIP6

Xiaorui Li
, Bo Liu
, Bin Wang
, Guoyu Ren
, Cristian Martinez-Villalobos
, Meiyu Chang
, Zhongshi Zhang

Research output: Contribution to journal › Article › peer-review

Abstract

This paper evaluates the biases, defined as deviations from ERA5, in extreme precipitation linked to tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) in Coupled Model Intercomparison Project Phase 6 (CMIP6) models during 1985–2014. Overall, the multi-model ensemble significantly overestimates the intensity of storm-associated extreme precipitation, with global average biases of 19.1% for TCs, 11.4% for ETCs, and 12.2% for ARs. This corresponds to at least a 57% higher risk of storm-associated extreme precipitation above the 99.9th percentile in the models. The skill score of the CMIP6 multi-model ensemble mean ranks in the mid-range among the models, while high-resolution models typically achieve higher scores. Composite rainfall structure analysis indicates that, although CMIP6 models roughly reproduce the overall structure of extreme precipitation associated with the three storm types, they display significant wet biases with pronounced regional features. These regional differences are primarily influenced by flawed dynamical processes, while moisture conditions play a secondary role.

Original language	English
Article number	e2025GL115365
Journal	Geophysical Research Letters
Volume	52
Issue number	15
DOIs	https://doi.org/10.1029/2025GL115365
State	Published - Aug 16 2025
Externally published	Yes

Funding

This study was supported by the National Natural Science Foundation of China (No. 42125502), Sichuan Science and Technology Program (No. 25LHJJ0328), National Natural Science Foundation of China (Nos. 42375037, 42005118, 42205008), the Data Observatory Foundation ANID Technology Center (No. DO210001), the Proyecto ANID Fondecyt Iniciación 11250471, the Key Grant Project of Science and Technology Innovation Capacity Improvement Program of CUIT (KYTZ202501) and the National Key Scientific and Technological Infrastructure Project Earth System Numerical Simulation Facility (EarthLab, Nos. 2023‐EL‐PT‐000465, 2024‐EL‐PT‐000781). We sincerely thank the editor, Dr. Christina Patricola, for her guidance throughout the review process, and we are also grateful to the two anonymous reviewers for their insightful and constructive comments, which have greatly improved the quality of this manuscript.

Keywords

atmospheric river
extratropical cyclone
extreme precipitation
model bias
storm systems
tropical cyclone

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10.1029/2025GL115365

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title = "Understanding the Intricacies of Model Biases in Storm-Related Extreme Precipitation Using CMIP6",

abstract = "This paper evaluates the biases, defined as deviations from ERA5, in extreme precipitation linked to tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) in Coupled Model Intercomparison Project Phase 6 (CMIP6) models during 1985–2014. Overall, the multi-model ensemble significantly overestimates the intensity of storm-associated extreme precipitation, with global average biases of 19.1\% for TCs, 11.4\% for ETCs, and 12.2\% for ARs. This corresponds to at least a 57\% higher risk of storm-associated extreme precipitation above the 99.9th percentile in the models. The skill score of the CMIP6 multi-model ensemble mean ranks in the mid-range among the models, while high-resolution models typically achieve higher scores. Composite rainfall structure analysis indicates that, although CMIP6 models roughly reproduce the overall structure of extreme precipitation associated with the three storm types, they display significant wet biases with pronounced regional features. These regional differences are primarily influenced by flawed dynamical processes, while moisture conditions play a secondary role.",

keywords = "atmospheric river, extratropical cyclone, extreme precipitation, model bias, storm systems, tropical cyclone",

author = "Xiaorui Li and Bo Liu and Bin Wang and Guoyu Ren and Cristian Martinez-Villalobos and Meiyu Chang and Zhongshi Zhang",

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AU - Li, Xiaorui

AU - Liu, Bo

AU - Wang, Bin

AU - Ren, Guoyu

AU - Martinez-Villalobos, Cristian

AU - Chang, Meiyu

AU - Zhang, Zhongshi

PY - 2025/8/16

Y1 - 2025/8/16

N2 - This paper evaluates the biases, defined as deviations from ERA5, in extreme precipitation linked to tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) in Coupled Model Intercomparison Project Phase 6 (CMIP6) models during 1985–2014. Overall, the multi-model ensemble significantly overestimates the intensity of storm-associated extreme precipitation, with global average biases of 19.1% for TCs, 11.4% for ETCs, and 12.2% for ARs. This corresponds to at least a 57% higher risk of storm-associated extreme precipitation above the 99.9th percentile in the models. The skill score of the CMIP6 multi-model ensemble mean ranks in the mid-range among the models, while high-resolution models typically achieve higher scores. Composite rainfall structure analysis indicates that, although CMIP6 models roughly reproduce the overall structure of extreme precipitation associated with the three storm types, they display significant wet biases with pronounced regional features. These regional differences are primarily influenced by flawed dynamical processes, while moisture conditions play a secondary role.

AB - This paper evaluates the biases, defined as deviations from ERA5, in extreme precipitation linked to tropical cyclones (TCs), extratropical cyclones (ETCs), and atmospheric rivers (ARs) in Coupled Model Intercomparison Project Phase 6 (CMIP6) models during 1985–2014. Overall, the multi-model ensemble significantly overestimates the intensity of storm-associated extreme precipitation, with global average biases of 19.1% for TCs, 11.4% for ETCs, and 12.2% for ARs. This corresponds to at least a 57% higher risk of storm-associated extreme precipitation above the 99.9th percentile in the models. The skill score of the CMIP6 multi-model ensemble mean ranks in the mid-range among the models, while high-resolution models typically achieve higher scores. Composite rainfall structure analysis indicates that, although CMIP6 models roughly reproduce the overall structure of extreme precipitation associated with the three storm types, they display significant wet biases with pronounced regional features. These regional differences are primarily influenced by flawed dynamical processes, while moisture conditions play a secondary role.

KW - atmospheric river

KW - extratropical cyclone

KW - extreme precipitation

KW - model bias

KW - storm systems

KW - tropical cyclone

UR - https://www.scopus.com/pages/publications/105012462264

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JO - Geophysical Research Letters

JF - Geophysical Research Letters

IS - 15

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ER -

Understanding the Intricacies of Model Biases in Storm-Related Extreme Precipitation Using CMIP6

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