Estimates of Rare Rainfall Extremes in Ungauged Areas

The probability estimation of rare or yet unobserved events is essential for hazard quantification, especially in the frequent case of short observational records. Data limitations can be mitigated by using regionalization techniques, which augment observational information, and by employing effective statistical models, such as the Metastatistical Extreme Value Distribution (MEVD), which maximizes the use of available observations. In this work, we develop the MEVD Regionalized framework (MEVD-R), with the aim of reducing the uncertainty in estimating the probability of rare events that are not present, or are not well sampled, in the observational record. Extensive testing using a global data set of 40,000 rain gauges across Europe, North America, and Australia demonstrates that MEVD-R yields negligible systematic error and greatly reduces predictive uncertainty, quantified via the use of independent test data subsets, compared to traditional approaches. The MEVD-R framework proves to be useful in data-scarce areas, even when conventional methods fail.