Estimating the probability of rare or yet unobserved rainfall extremes is essential for hazard quantification, especially in the context of an intensifying water cycle and of short observational records, common even in countries with long hydro-meteorological monitoring tradition. 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) to reduce uncertainty in estimating the probability of rare events that are not present in the training sample. Extensive testing using a global dataset of 40,000 rain gauges across Europe, North America, and Australia demonstrates that MEVD-R yields negligible systematic error and greatly reduces uncertainty compared to traditional approaches. MEVD-R can even provide estimates when available data are too sparse for conventional methodologies to offer reliable results.