A Statistical Method to Model Non‐stationarity in Precipitation Records Changes
Abstract In the context of climate change, assessing how likely a particular change or event was caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (EEA) methodology to analyze yearly maxima records, key indicators of c...
| Published in: | Geophysical Research Letters |
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| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2025
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| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023GL107201 https://doaj.org/article/252ac84f39064c9ab90204ab85858af4 |
| Summary: | Abstract In the context of climate change, assessing how likely a particular change or event was caused by human influence is important for mitigation and adaptation policies. In this work we propose an extreme event attribution (EEA) methodology to analyze yearly maxima records, key indicators of climate change that spark off media attention and research in the EEA community. Although they deserve a specific statistical treatment, algorithms tailored to record analysis are lacking. This is particularly true in a non‐stationary context. This work aims at filling this methodological gap by focusing on records in transient climate simulations. We apply our methodology to study records of yearly maxima of daily precipitation issued from the numerical climate model IPSL‐CM6A‐LR. Illustrating our approach with decadal records, we detect in 2023 a clear human induced signal in half the globe, with probability mostly increasing, but decreasing in the south and north Atlantic oceans. |
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