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The NAO’s correlation with precipitation in Norway and the Iberian Peninsula is well established, yet its explanatory power diminishes across much of Europe. Other patterns may drive precipitation variability in these regions, but traditional methods for identifying circulation-precipitation relationships have limitations. Prescribed indices assume a causal link between specific sites and physically coherent structures, whereas EOF methods impose linearity and orthogonality constraints that atmospheric circulation does not obey. This study identifies weather regimes associated with precipitation extremes across representative European regions using a non-linear, non-orthogonal, data-driven approach.Specifically, we employ a machine learning approach that discovers weather regimes directly from mean sea level pressure fields, without prescribing their structure a priori. The method builds upon Spuler et al. 2024 & 2025, with changes that allow for larger, higher-resolution input domains. It identifies distinct atmospheric states associated with different precipitation intensities at target locations, linking discovered patterns directly to their impacts. Importantly, once regimes are identified, indices analogous to traditional teleconnection indices can be derived, enabling comparison with established frameworks while capturing dynamics they may miss.We apply this method to daily ERA5 fields, targeting precipitation in selected European regions with contrasting dynamical drivers, including Bergen, the Iberian Peninsula, and Copenhagen. This allows us to present teleconnection patterns identified through this approach over the entire Northern Hemisphere as well as relevant sub-regions, including the North Atlantic and Arctic, focusing on extreme precipitation drivers. We find multiple regimes that resemble different flavors of the well-known NAO pattern, alongside circulation states consistent with blocking-like structures. Comparisons with traditional EOF analysis highlight the effects of relaxing linearity and orthogonality constraints. Correlation maps are produced for both methods, enabling direct evaluation of how the data-driven regimes compare to established EOF-based patterns.The non-linear, data-driven framework remains physically interpretable and avoids the limitations of linear orthogonal decomposition. Though currently applied to ERA5, the approach transfers directly to CMIP6 historical and scenario runs, enabling assessment of how regime frequencies and precipitation associations may shift under climate change. Overall, this study illustrates how ML-based approaches can complement traditional synoptic climatology by allowing circulation–impact relationships to emerge directly from the data.BibliographySpuler, Fiona R. et al. (2024): Identifying probabilistic weather regimes targeted to a local-scale impact variable. Environmental Data Science, 3, e25.Spuler, Fiona R. et al. (2025): Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders. Weather and Climate Dynamics, 6, 995-1014.