A Multimethod Attribution Analysis of Spain's 2024 Extreme Precipitation Event

Année de publication

2025

Between 28 October and 4 November 2024, Spain experienced an extreme precipitation event with new national records for subdaily precipitation intensity. The event, driven by a cutoff low and moist influx from a warm Mediterranean Sea, triggered devastating floods and severe socioeconomic impacts, leading to 237 fatalities and EUR 17 billion in estimated economic losses. In this study, we conduct a comprehensive attribution analysis that integrates complementary methods with varying levels of conditionality to assess the influence of climate change on this extreme event. They include unconditional probabilistic approaches, flow-conditioned analogs, and highly conditioned storylines. Unconditional probabilistic methods found no discernible anthropogenic influence on the probability and magnitude of such events at local and regional scales, aligning with historical extreme precipitation trends in Spain. In contrast, dynamical conditioning using low pressure systems similar to the observed one revealed detectable climate change-related increases in precipitation, partially mediated by warm western Mediterranean sea surface temperatures. Highly conditioned storylines uncovered robust signals in precipitation and the thermodynamic drivers of the event, including human-induced moistening, but weak or nonrobust responses in dynamical aspects. These findings highlight the complexity of attributing convective extremes and the intricate interplay between dynamical and thermodynamic factors. The study underscores the complementarity, as opposed to the conflict, of different attribution approaches and illustrates the importance of combining attribution methods with varying levels of conditionality to provide a comprehensive understanding of climate change's role in extreme events. Significance Statement The extreme rainfall of October 2024 in Spain caused hundreds of fatalities and raised pressing questions about the role of climate change. We use observations, reanalysis, and data-driven weather forecasts to address changes in the frequency of threshold-crossing events, the severity of similar events, and the synoptic drivers of the actual event. These attribution questions yield different but reconcilable answers, with consistent climate change signals emerging when the atmospheric conditions of the event are considered. Our results highlight the importance of addressing the full spectrum of attribution questions and provide guidance for collecting multiple evidences through a comprehensive integration of climate-to-weather approaches with varying degree of conditionality on the observed event.</div>

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