Work

Heatwaves in a 15 °C World: Exploring remote drivers’ influence on Northern Italy with a storyline-based approach

Year: 2026

Type: article

Abstract: Heatwaves (HW) heavily affect several sectors of the economy and society in the European and Mediterranean regions, and particularly in the Po Valley area. Their frequency and intensity are projected to increase, and a thorough understanding of how this occurs in relation to rising global temperatures and to the projected changes in other weather and climate variables is fundamental for developing effective adaptation plans. In this study, developed within the Climate Intelligence (CLINT) project, we propose an application of the storylines technique, empowered by the definition of shared global thermodynamical conditions across the simulations (Common Temperature States, CTS) and by the implementation of an ML-based driver-oriented validation for the used CMIP6 models. This allows us to produce a set of physically sound storylines, where the increase of heatwave-related indices is inspected under specific conditions in the evolution of other drivers, researching proximate drivers and teleconnections. Our results highlight relevant contributions coming from precipitation depletion over three different Euro-Mediterranean domains (Northern Africa, Western and Eastern Europe), which is associated with enhanced sensible heat fluxes and co-occurs with the largest increases in the HW Magnitude Index and the number of HW days. Furthermore, the increase of these indices is found to be connected with different confidence levels to tropical modes such as El Niño Southern Oscillation and the convective activity in the Western Pacific, whose upper-level divergence triggers planetary wave trains. By contrast, the pressure gradients in the North Atlantic appear to be positively correlated with HW in Northern Italy, but are of secondary importance with respect to precipitation decrease. These findings demonstrate how the use of Machine Learning tools (here, the Spatio-Temporal Cluster-Optimised Feature Selection (STCO-FS) framework) can reinforce the storylines approach and advance scientific understanding of extreme events in a changing climate.

Source: Weather and Climate Extremes

DOI: 10.1016/j.wace.2026.100949