This talk presents a method for using major flash flood events that occurred in ungauged catchments to reduce the uncertainties in estimating regional flood quantiles. This is a methodological contribution for a better description of the flash flood hazard based on a more efficient valuation of the various sources of possible information. The method is based on standard regionalization methods assuming that the flood peak distribution rescaled by a site-dependent index flood is uniform within a homogeneous region. A likelihood formulation and a Bayesian Markov Chain Monte Carlo (MCMC) algorithm are used to infer the parameter values of the regional distributions. This statistical inference technique has been selected for its rigorousness various hypotheses are explicitly formulated in the likelihood function, its flexibility as for the type of data that can be treated, and its ability to compute accurate estimates of the confidence intervals for the adjusted parameters and for the corresponding flood quantiles. The proposed method is applied to data sets from the South of France that include series of annual peak discharges.