Convection-permitting simulations for the attribution of extreme precipitation events in Emilia-Romagna

The work presented in this thesis analyzes the impact of anthropogenic climate change on the extreme precipitation events that struck Emilia-Romagna and Northern Adriatic coast between April 30 and May 4, 2023, and from September 17 to 20, 2024. To this end, a novel extreme event attribution methodology is implemented, combining the storyline approach with spectral nudging and the pseudo-global warming (PGW) technique. The events are simulated using the high-resolution convection-permitting model MOLOCH (1.8 km) under both the present-day (factual) climate and a hypothetical pre-industrial (counterfactual) scenario in absence of global warming. The pre-industrial simulations are obtained by applying a thermodynamic perturbation derived from high-resolution (9 km) global climate simulations produced within the Destination Earth project, which provide the large-scale atmospheric conditions used in the storyline framework. The analysis focuses on identifying the added value of MOLOCH for the simulation of the events, on isolating the thermodynamic contribution of climate change, and evaluating how increased temperature and atmospheric moisture content can intensify extreme precipitation. The results highlight substantial differences between the two analyzed events. The May 2023 event, dominated by well-defined dynamical drivers and orographic forcing, shows a linear increase in precipitation in the factual simulation, primarily attributable to the intensification of moisture fluxes toward the affected area. In contrast, the September 2024 event, characterized by intense convective activity, presents a more complex response, appearing to be linked to the interaction between higher moisture fluxes and thermodynamic instability. In this case, the increase in precipitation emerges clearly only in the high-resolution, convection-permitting simulations, highlighting the importance of explicit convection representation for an accurate attribution of extreme events.