Traditional catastrophe (CAT) risk models rely on costly manual construction to produce extreme‑weather scenarios, a workflow that has changed little since the 1990s. As climate extremes intensify, the entire risk‑transfer chain faces mounting cost and timeliness challenges.\ \ This paper introduces the TAISE framework, which repurposes existing AI weather‑forecasting models for scenario generation. By iteratively generating continuous global atmospheric fields, extreme events emerge naturally without the need for separate snapshot creation.\ \ A proof‑of‑concept experiment demonstrates that TAISE reduces computational expense by roughly an order of magnitude compared with conventional methods, while preserving temporal continuity and cross‑regional correlations that snapshot‑based approaches typically miss.\ \ These findings suggest that AI‑driven scenario emergence can democratize catastrophe risk quantification, enabling insurers, reinsurers, ILS fund managers, and public‑sector risk managers to perform dynamic, comprehensive portfolio assessments.\ \ Review