Extreme weather losses are increasing worldwide, driven by a combination of growth in socioeconomic exposure and hazards influenced by climate change. This trend is acutely felt in the insurance and reinsurance sectors, where severe convective storms, floods, wildfires, and tropical cyclones are generating unprecedented and compounding financial burdens. A central challenge is that traditional catastrophe models rely heavily on historical event statistics that may no longer represent current or emerging climate regimes. At the same time, global climate model simulations providing insight into long-term projections are currently available at spatial and temporal scales that are too coarse for risk pricing, solvency assessment, and local adaptation planning. This perspective argues for a more formal integration of high-resolution downscaling, ensemble modeling, and catastrophe risk analysis to bridge this gap. High-resolution downscaling approaches can resolve local-scale peril physical processes—such as those related to wildfire, extreme rainfall, tropical cyclones, and hail—that are largely unresolved by global climate models. Large ensembles provide a probabilistic understanding of hazard sensitivity to internal variability and different forcing pathways, information that aligns naturally with the insurance industry’s need to quantify tail risks. Moreover, closer collaboration between atmospheric scientists, climate modelers, and industry practitioners is essential to ensure that model assumptions, uncertainties, and data requirements are mutually understood. Severe convective storms, the fastest-growing peril in U.S. insurance markets, are used as an illustrative example of where scientific advances and industry needs are rapidly converging. Finally, research priorities and policy implications are outlined to help enhance climate-resilient risk management and close the global protection gap.

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