Reproducing extremes in continuous stochastic precipitation series

Weather generators are widely used in impact and risk assessment studies to produce long synthetic series of meteorological variables that reproduce current or future climate statistics and natural variability. Most stochastic weather generators are trained to well reproduce the bulk of the precipitation distribution, but they often fail to adequately represent extremes, leading to poor performance in flood hazard and hydrological risk applications. This limitation becomes particularly critical under climate change, as projected impacts on precipitation are expected to manifest differently for ordinary and extreme precipitation values. Here, we address this issue by integrating parametric Weibull tails estimated using the Simplified Metastatistical Extreme Value (SMEV) approach in ordinary weather generator series using a quantile mapping.