MORE, a new convection-permitting reanalysis dataset over Italy and Alpine region: Validation and application in weather, climate and hydrology

Abstract. This study presents a new convection-permitting reanalysis dataset over Italy and Alpine region, produced through dynamical downscaling of ERA5 reanalysis with the non-hydrostatic mesoscale model MOLOCH. MORE (MOloch-downscaled ERA5 REanalysis) is a very high spatial resolution (~1.7 km) gridded dataset, covering the 1990–present period. The dataset includes hourly outputs of a wide range of variables at the surface and on pressure-levels.

MORE validation follows a multiscale framework applied to precipitation and near-surface air temperature using dense and quality-controlled observational datasets. MORE is benchmarked against other convection-permitting products and coarser- resolution reanalyses. Results show that MORE realistically reproduces spatial and temporal variability and improves the simulation of wet-hour frequency, precipitation intensity, sub-daily extremes, particularly during convective regimes, and key climate indicators such as the number of tropical nights, although a systematic cold bias is present in temperature.

As an application example, the May 2023 Emilia-Romagna (northern Italy) catastrophic flood is analyzed. MORE successfully reproduces the meteorological evolution of the two heavy rainfall events, providing added value in the representation of the mesoscale features resulting in localized precipitation extremes. In cascade, hydrological simulations driven by MORE data improve the representation of catchment-scale discharge, and soil moisture dynamics.

Overall, MORE represents the highest-resolution convection-permitting reanalysis currently available for Italy and the Alpine region. Its comprehensive set of variables at hourly resolution makes it a valuable reference for hydrometeorological studies, climate change adaptation, and climate services in regions with complex terrain and high exposure to extremes. The dataset is openly available at DOI: https://doi.org/10.5281/zenodo.18470948 (Stocchi, P. 2026) and will be periodically updated to ensure long-term accessibility, reliability and completeness.