Integrating AI for Climate Resilience

As climate extremes intensify, the gap between hazard detection and effective anticipatory action remains a critical bottleneck for resilience. This talk synthesizes two perspectives works to outline a roadmap for the next generation of AI models for the analysis, modeling and understanding of extreme events, and their integration in Early Warning Systems (EWS). We first examine the role of deep learning and Explainable AI (XAI) in advacing the detection and physical understanding of extreme weather, ensuring transparency in high-stakes risk assessment. We then propose advancing towards an integrated EWS architecture, leveraging Meteorological and Geospatial foundation models to predict multi-hazard impacts. By embedding causal AI to ensure reliable reasoning and generative methods for long-term adaptation, these digital technologies may provide a robust framework for simulating hazard cascades and delivering equitable, people-centered disaster response.