Emerging intra-annual sequences of climate extremes in Europe

Climate change drives an increase in the frequency of multiple meteorological extreme event types (e.g., extreme precipitation, storms, droughts, heatwaves) by affecting thermodynamic and dynamic processes in the coupled land-atmosphere system. For example, the extended droughts during 2018-2020 in Europe, flooding triggered by extreme precipitation in Germany in 2021, as well as Valencia and central France in 2024, or prolonged heatwaves in 2003, 2015, 2018, and 2022 across continental Europe had strong adverse impacts on socio-economic systems and the environment. Given a higher frequency of extreme events, it becomes more likely that regions experience events of the same or different types in consecutive seasons, thereby challenging the regions’ short-term coping and recovery ability and long-term resilience.

While extreme events are generally well-studied, holistic analyses of typical sequences of extreme events are missing. Compound analyses commonly focus on specific combinations of events, but usually miss typical intra-annual sequences of extreme events with the potential for high impacts.

Our analysis addresses the question 1) which sequences of extremes occur most often, 2) how robust they are, and 3) their physical implications. We assess intra-annual sequences of extreme seasons on the European scale in a regional multi-member ensemble of the Canadian Regional Climate Model version 5 (CRCM5) covering the European CORDEX domain at a high spatial resolution (0.11°, 12 km). The CRCM5 was driven by four members of the Max-Planck-Institute Grand Ensemble (MPI-ESM-LR) under SSP3-7.0. Given that the four members differ only by initial conditions and thus share the same climate, this setup quadruples the sample size for finding extreme events. We selected extreme event indicators for extreme heat, droughts, extreme precipitation and wind. They cover hazards of regionally varying importance, but each of them poses considerable risks to human and natural systems in Europe. The sequences of extreme events were derived using the sequential pattern mining algorithm cSPADE.

In this contribution, we show first findings on the most prevalent sequences of seasonal events under SSP3-7.0. We map vulnerability hotspots associated with intra-annual extreme event characteristics and present physical “stories” corresponding to the sequences. Furthermore, we aim to provide the basis for understanding potential interrelations of seasonal extreme events.