Improving Our Understanding of Global Severe Convective Storm Hazards

Severe convective storms (SCSs) are responsible for substantial societal and economic losses around the world. These impacts are rising—primarily due to increasing exposure—yet the underlying hazard remains deeply uncertain, even in regions with dense observational networks. This uncertainty complicates efforts to anticipate and manage risk. In this keynote, we will present recent advances aimed at enhancing our understanding of global SCS hazards through the use of both statistical and dynamical modeling approaches. Statistical models, including machine learning techniques, are increasingly used to estimate hail hazard at continental to global scales, providing valuable insights into large-scale patterns and regional variability. Complementing these efforts, convection-permitting simulations—run at kilometer-scale resolution on regional and global scales—allow for the explicit representation of severe convective processes. These include hail, straight-line winds, and supercell thunderstorms, often accompanied by diagnostic tools like HAILCAST, which helps better assess event intensity and frequency. We will discuss the strengths and weaknesses of these approaches and highlight how combining them provides a more comprehensive understanding of SCS behavior and its sensitivity to environmental drivers. We will conclude by highlighting open scientific questions and future research directions, particularly in consideration of ongoing and projected climate changes, to support improved hazard assessment and risk reduction strategies globally.