Abstract
<jats:p>Mediterranean heatwaves are among the most impactful extreme weather and climate phenomena, with significant impacts on human health, ecosystems, energy demand, and water resources. The Mediterranean basin is widely recognized as a climate change hotspot, where the frequency and intensity of summer temperature extremes are projected to increase. However, important uncertainties remain regarding the representation and predictability of heatwave-related atmospheric conditions at seasonal timescales.This study investigates major Mediterranean heatwave events during the 1991–2010 period using high-resolution, dynamically downscaled hindcast simulations produced with the Weather Research and Forecasting (WRF) model, driven by ERA5 and CFSR reanalysis data. Heatwave conditions are identified using ETCCDI indices, e.g. TX95p and TX99p, enabling a consistent characterization of moderate and extreme temperature events in terms of intensity, duration, and spatial extent across the Mediterranean region.The analysis focuses on a set of prominent historical heatwave episodes within the study period and examines their associated large-scale atmospheric conditions and regional circulation features. Particular attention is given to the ability of the simulations to reproduce the timing, spatial patterns, and persistence of these events, as well as the broader atmospheric environments in which they develop.To evaluate model performance and predictability, all WRF-based simulations are analyzed at a common 3-month lead time, allowing for a uniform examination of their ability to reproduce heatwave-relevant circulation regimes and extreme temperature characteristics. This includes an evaluation of event representation, spatial patterns, persistence, and associated large-scale circulation features. The comparison provides insight into the capabilities and limitations of dynamical downscaling for representing Mediterranean heat extremes.Overall, the study aims to improve the understanding of Mediterranean heatwave behavior and to evaluate the capability of regional dynamical downscaling systems and seasonal forecast models to represent and anticipate extreme summer temperature conditions.Acknowledgements: This research was supported by the PREVENT project that has received funding from the EU Horizon Europe framework programme (grant no. 101081276) / Part of the results presented in this work have been produced using the Aristotle University of Thessaloniki (AUTh) High Performance Computing Infrastructure and Resources.</jats:p>