Accurately describing precipitation extremes is an essential information for computing hazard indicators. In this context, the characteristics and quality of data provided by VHR-REAIT (Very High-Resolution RE-Analysis for Italy), an open-access, high-resolution hourly climate dynamical downscaling of the ERA5 reanalysis, refined to a 2. 2 km spatial resolution using the COSMO-CLM Regional Climate Model (RCM) is investigated. The dataset, produced by the CMCC Foundation, REMHI division, within the framework of the HIGHLANDER Project (highlanderproject. eu), covers the entire Italian territory and surrounding areas, ensuring complete regional representation. It may be accessed via the CMCC Data Delivery System (DDS) at the link: https: //dds. cmcc. it/#/dataset/era5-downscaled-over-italy/. The analysis considers 10-years of observation data (from January 1, 2014, to December 31, 2023) of the high frequency rain gauge network of 'Agenzia Prevenzione Ambiente Energia Emilia-Romagna' (ARPAE). The data is available at 30-minute time intervals, to match the hourly resolution of reanalysis used for the comparison. This has allowed to analyse precipitation extremes at multiple time scales (from 1 to 12 hours). Results indicate that precipitation extremes (represented by the 95th percentile for each data set) are realistically characterised by the model in terms of their inter-time scale links and that only a minority of events are characterized with extremes at different timescales, indicating the need of data with adequate time resolution in relation to practical applications (e. g. , early warning systems). However, the comparisons between reanalysis and observations shows a limited consensus on timing of extreme events, particularly if short (hourly) time scales are considered, with significant spatial variability among stations. For instance, some stations show around 60% consensus on timing for daily precipitation extremes, while other stations show only 10% consensus for hourly precipitation extremes. Moreover, the reanalysis data also shows an over-representation of events for the lower quartile group of the 95th percentile, while the upper quartiles are under-represented. It found that the consensus of the model’s prediction is generally better for daily precipitation extremes, progressively decreasing as the time scale becomes shorter. These results can help improve both early warning systems and future projections based on climate model outputs (e. g. , CMIP6 or the COSMO-CLM RCM), by estimating how precipitation extremes begave at different time scales, thereby improving risk management and mitigation strategies, particularly at short time scales.
Carlo et al. (Fri,) studied this question.
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