Climate-Conditioned Catastrophe Modeling for Dynamic Risk Assessment

Seasonal climate variability strongly influences North Atlantic hurricane activity, yet catastrophe models used across risk transfer markets typically assume a stationary long-term climatology. This limits the ability of risk-transfer systems — spanning insurance, reinsurance, capital markets, and public risk financing — to anticipate predictable fluctuations in hazard and loss potential. We introduce a climate-conditioned catastrophe modeling framework that integrates probabilistic seasonal hurricane forecasts with stochastic event sets to generate dynamically updated loss distributions. The framework combines a climate-forced tropical cyclone generator with a statistical resampling engine that adjusts event frequency, intensity, and landfall patterns in catastrophe model output to reflect the expected climate state of each season. Applying this approach to a 40-year out-of-sample evaluation, we show that climate-conditioned loss distributions substantially improve the performance and resilience of insurance-linked securities portfolios. Dynamic portfolios informed by seasonal climate states achieve up to 30% higher mean returns and 50% smaller drawdowns during high-loss years compared with portfolios based on static climatology. These results demonstrate that seasonal climate information can be operationalized to enhance financial stability in sectors exposed to weather extremes. The methodology is hazard-agnostic and extensible to other climate-sensitive perils.