Evolution of extreme precipitation in Spain: contribution of atmospheric dynamics and long-term trends

21 Pags.- 11 Figs.- 3 Tabls. Data availability: No datasets were generated or analysed during the current study. © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License.

The analysis of temporal changes in extreme event attributes, specifically magnitude and frequency, is hindered by the rarity and exceptional nature of the events being studied. The non-stationary extreme value theory (NSEVT) provides a well-established framework for assessing how extreme event probabilities vary as a function of one or more covariates. This study employs NSEVT to investigate the recent evolution and primary drivers of extreme precipitation in Spain, utilizing indices of three large-scale modes of atmospheric circulation and time as covariates. A non-stationary peaks-over-threshold model is applied to an observational network comprising 341 weather stations over the period 1951–2020. The results demonstrate that a multivariate model accounting for the influences of all covariates fits the data significantly better than simpler, univariate and stationary models in the majority of stations. The multivariate model effectively captures the spatial and temporal marginal influences of atmospheric dynamics on the magnitude-frequency relationship of different attributes of extreme precipitation events, including daily peak intensity and accumulated event precipitation. In contrast, the marginal influence of time is relatively small and sparse, lacking a spatially coherent pattern. Notably, the multivariate model reveals larger temporal influences than those inferred from the univariate model, with more stations displaying significant decreases than increases in extreme precipitation event attributes. These findings highlight the importance of considering multiple covariates and non-stationarity when analyzing temporal changes in extreme events.

This research work has been funded by the Spanish National Research Agency (MCIN/AEI) through project PID2020-116860RB-C22 and R.S.-N. personal grant RYC2021-034330-I, by the European Commission - NextGenerationEU (Regulation EU 2020/2094) through CSIC’s Interdisciplinary Thematic Platform “Clima (PTI Clima) / Development of Operational Climate Services”, and by Aragón Government through grant E02-20R.

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.

Peer reviewed