The impacts of cities and climate warming on extreme rainfall under strong synoptic conditions are not well understood. Here, we carry out the first model‐based assessment of urban impacts on extreme flood‐producing storms for the European region. We identify contrasting roles of cities and climate warming in determining the space‐time variability of the July 14, 2021 storm over western Europe. While climate warming dominates the temporal rainfall variability over the domain, cities further enhance total rainfall over their suburbs by dynamically modifying the intensity and position of moisture convergence. There is a cyclonic structure of flow anomalies around the city induced by urban surface roughness. The rainfall anomaly induced by the interactive impacts of cities and climate warming is 50% larger than by those urban impacts alone. We highlight a need to develop a regional framework by reconciling the emerging urban‐rural contrasts in vulnerability and preparedness to hydrometeorological extremes.

Alpine basins are important water sources for human life, and reliable hydrological modeling can enhance the water resource management in alpine basins. Recently, hybrid hydrological models, coupling process-based models and deep learning (DL), have exhibited considerable promise in hydrological simulations. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information within the basin and describe alpine hydrological processes, which restricts their applicability in hydrological modeling in large alpine basins. To address this issue, we develop a set of hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. A climate perturbation method is further used to test the applicability of the hybrid models to analyze the hydrological sensitivities to climate change in large alpine basins. Results indicate that proposed hybrid hydrological models can perform well in predicting runoff processes and simulating runoff component contributions in large alpine basins. The optimal hybrid model with Nash–Sutcliffe efficiencies (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. In addition, the results also show reasonable patterns in the analysis of the hydrological sensitivities to climate change. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about the hydrological sensitivities to climate change in large alpine basins.