Climate science has long relied on simulated and projected events to populate the statistics of climate variables, using models to generate large synthetic populations of events from which the moments of a climate distribution (mean, variance, and especially its tail) can be estimated, precisely because short observational records cannot sample rare extremes with any statistical robustness. This study applies that same strategy to Mediterranean hurricanes, or "Medicanes": rare, high-impact tropical-like cyclones bringing intense rainfall to densely populated coastal regions, for which too few real cases exist to build a reliable climatology. Building on a statistical-deterministic downscaling methodology used for over a decade to translate global climate model output into storm-scale hazard information , from early applications on CMIP3 simulations (Romero and Emanuel 2013) to CMIP5-based projections (Romero and Emanuel 2017) , we generate thousands of synthetic medicane tracks from reanalysis data and global climate models. Coupling these tracks with a physics-based tropical cyclone rainfall algorithm produces spatially and temporally resolved precipitation fields, from which the full statistical distribution of medicane rainfall and its associated return periods , can be estimated, even for magnitudes far beyond anything recorded to date. The approach is validated against reanalysis, satellite, and radar observations, and sensitivity analyses probe how synthetic sample size and algorithmic choices affect the resulting statistics. Under the RCP8.5 scenario, the resulting hazard distribution shifts markedly: 250-year rainfall along the Adriatic coast increases by up to 160 mm, and some 500-year events are projected to exceed 800 mm of total rainfall. By showing how synthetic event generation can be used to populate and characterize the statistics of a rare climate hazard, this work offers a methodological blueprint directly relevant to AI- and data-driven approaches now being developed to model climate extremes for which observations alone will always be insufficient.

Saraceni, M., L. Silvestri, P. Bongioannini Cerlini, R. Romero, and K. Emanuel, 2026: Estimating Medicane Precipitation Hazard. Journal of Climate, in press, https://doi.org/10.1175/JCLI-D-25-0364.1.

TBD