Exploring AI-Driven Event-based Storylines

Artificial Intelligence-based Numerical Weather Prediction (AI-NWP) models have recently emerged as powerful tools for weather forecasting, offering computational efficiency and high accuracy. This study explores the extreme weather events simulated by the Artificial Intelligence Forecasting System (AIFS), initialised with conditions derived from kilometer-scale storyline experiments using the IFS-FESOM model ─ where the atmospheric circulation is constrained to observations. We present two case studies: the 2023 South Asian humid heatwave and the 2024 Storm Boris. These two events are reproduced in the present climate, but also simulated if they were to unfold in pre-industrial and +2K future climates, effectively creating AI-driven storylines. The methodology we employ offers a complementary framework, where the use of AI-driven ensembles provides a scalable and rapid way to assess the potential uncertainty and variability associated with such events, by enabling us to explore a broader range of plausible outcomes at very low computational costs. By combining the strengths of physics-based modelling with the efficiency and flexibility of AI-driven simulations, this dual approach offers a pathway to operationalise ensemble-based extreme weather storylines.