Analysis and comparison of extreme precipitation events between physical models and Artificial Intelligence models

In recent decades, extreme meteorological events have increased in frequency and intensity, enhancing hydrogeological risk. This study evaluates the performance of a Machine Learning model based on a Latent Diffusion Network (Latent Diffusion Model, LDM), developed within the RETE project, a joint initiative of FBK and ENEA, in generating high-resolution precipitation fields over Italy. Four historical precipitation datasets produced by the LDM are compared with the main reanalysis products, ERA5 and CERRA, to assess their ability to reproduce precipitation climatology and extreme events. The analysis is based on standard climatological statistics and Extreme Value Theory (EVT). Climatological features are examined through daily mean and seasonal cumulative precipitation, while extremes are investigated by estimating precipitation levels associated with 10, 20, and 50-year return periods. The results provide insight into the reliability of LDM-based products as complementary tools to traditional reanalyses for climate studies and potential operational applications.