Résumé
Extreme weather events in Europe are closely linked to the large-scale atmospheric circulation and often develop over several consecutive days. Most existing circulation-based approaches focus on identify extreme weather patterns as instantaneous atmospheric states and therefore do not explicitly account for the temporal evolution of the flow. In this study, we apply a data-driven and unsupervised methodology to identify rare atmospheric trajectories from reanalysis data. The method quantifies how isolated short segments of atmospheric evolution are within the space of all observed trajectories, using daily sea-level pressure fields over Europe. We apply the approach to several decades of reanalysis data and identify the most isolated trajectories for different trajectory lengths. The detected trajectories are characterised by large-scale circulation anomalies and strong pressure gradients. A comparison with independent databases of European extreme events shows a statistically significant overlap, particularly for windstorms. Increasing the trajectory length enhances the detection of multi-day events, indicating that the method captures persistent atmospheric evolutions rather than isolated states. In addition to windstorms, the detected trajectories correspond to cold spells and blocking-like circulation patterns, as well as events that are not systematically documented in existing pan-European databases. These results indicate that analysing rare atmospheric trajectories provides complementary information to state-based approaches and offers a general framework for the detection of extreme atmospheric evolutions in reanalysis datasets.