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• State of the art on precipitation frequency analysis based on remotely sensed datasets is depicted. • Investigation of climatic gradients and explicit inclusion of the areal scale is highly promising. • Current main issues: estimation accuracy, statistical methods, reference/validation methods. • Recommended research: uncertainty partition, dedicated statistical and validation methods. Information on extreme precipitation is essential to managing weather-related risks and designing hydraulic structures. Research attention to frequency analyses based on remotely sensed precipitation datasets, such as those obtained from weather radars and satellites, has been rapidly increasing owing to their potential to provide information for ungauged regions worldwide. Together with the ability to measure the areal scale directly, these analyses promise to overcome the sampling limitations of traditional methods based on rain gauges. This focused review of the literature depicts the state of the art after a decade of efforts, and identifies the crucial gaps in knowledge and methodology that currently hinder the quantitative use of remotely sensed datasets in water resources system design and operation. It concludes by highlighting a set of research directions promising immediate impact with regard to the separation of the sources of uncertainty currently affecting applications based on remotely sensed datasets, the development of statistical methods that can cope with the peculiar characteristics of these datasets, and the improvement of validation methods. Important gains in knowledge are expected from the explicit inclusion of the areal dimension in the analyses and from the fine-scale investigation of extreme precipitation climatology. [ABSTRACT FROM AUTHOR]

This study expands the Metastatistical Extreme Value (MEV) framework to sub-daily rainfall frequency analysis and compares it to extreme value theory methods in presence of short records and measurement errors. Ordinary events are identified based on the temporal autocorrelation of hourly data and modeled with a Weibull distribution. MEV is compared to extreme value theory methods in the estimation of long return period quantiles from actual data (160 rain gauges with at least 60-year record in the contiguous United States) and on synthetic data perturbed with measurement errors typical of remote sensing rainfall estimation. MEV tends to underestimate the 100-year return period quantiles of hourly rainfall when 5–20 years of actual data are used, but presents diminished uncertainty. When a good model of the ordinary events and adequate number of events per year are available, MEV is able to provide information on the 100-year return period quantiles from 10–20, or even 5 years of data with significantly reduced uncertainty (<30% uncertainty for 5-year records). MEV estimates of 100-year return period quantiles from short records are much less sensitive than extreme value theory methods to additive/multiplicative errors, presence of cap values in the estimates, and missing of extreme values. Results from this study strongly support the use of MEV for rainfall frequency analyses based on remotely sensed datasets. [ABSTRACT FROM AUTHOR]

• The Metastatistical Extreme Value Distribution (MEVD) framework applied to four short-record high-resolution quasi-global sub-daily precipitation products.. • MEVD parametric uncertainty and its propagation effect in the quantile is investigated. • Impact of spatial scale mismatch on the frequency analysis of extremes is quantified. • We provide an assessment of satellite-based precipitation products in different hydroclimatic regions. Precipitation extremes and associated hydrological hazards pose a significant global risk to society and economy. To be effective, mitigation strategies require the best possible estimation of the intensity and frequency of precipitation extremes. Traditional approaches to precipitation frequency analysis rely on long-term records from in-situ observations, which are limited in terms of global coverage. Satellite-based precipitation products provide global coverage, but errors in these estimates may lead to large biases in the quantification of extremes. Previous studies have demonstrated the ability of the novel Metastatistical Extreme Value Distribution (MEVD) framework to provide robust estimates of high quantiles in the presence of short-term data records and the uncertainties typical of remote sensing precipitation products. Here, we evaluate MEVD-based precipitation frequency analyses for four widely used quasi-global precipitation products (IMERG-v6, GSMaP-v6, CMORPH-v1.0, and MSWEP-v2) over high-density gauge networks in five hydroclimatic regions (Austria, Italy, Florida, Texas, and Arizona). We show dependence of MEVD-based estimation error on the characteristics of each dataset and the hydroclimatic region. Additionally, we evaluate the sub-grid variability of extreme precipitation and demonstrate the impact of spatial scale mismatch (that is, single in-situ gauge versus satellite pixel) on the frequency analysis of extremes. This work provides an assessment of the use of MEVD for estimating precipitation extremes from globally available datasets and an understanding of the variability of sub-daily precipitation extremes in different hydroclimatic regions of the world. [ABSTRACT FROM AUTHOR]