Nerantzaki, Sofia D.Sofia D.NerantzakiPapalexiou, Simon MichaelSimon MichaelPapalexiou2025-10-022025-10-022021-12-18Journal of Hydrology 605: 127302 (2022-2)https://hdl.handle.net/11420/57752Here we review methods used for probabilistic analysis of extreme events in Hydroclimatology. We focus on streamflow, precipitation, and temperature extremes at regional and global scales. The review has four thematic sections: (1) probability distributions used to describe hydroclimatic extremes, (2) comparative studies of parameter estimation methods, (3) non-stationarity approaches, and (4) model selection tools. Synthesis of the literature shows that: (1) recent studies, in general, agree that precipitation and streamflow extremes should be described by heavy-tailed distributions, (2) the Method of Moments (MOM) is typically the first choice in estimating distribution parameters but it is outperformed by methods such as L-Moments (LM), Maximum Likelihood (ML), Least Squares (LS), and Bayesian Markov Chain Monte Carlo (BMCMC), (3) there are less popular parameter estimation techniques such as the Maximum Product of Spacings (MPS), the Elemental Percentile (EP), and the Minimum Density Power Divergence Estimator (MDPDE) that have shown competitive performance in fitting extreme value distributions, and (4) non-stationary analyses of extreme events are gaining popularity; the ML is the typically used method, yet literature suggests that the Generalized Maximum Likelihood (GML) and the Weighted Least Squares (WLS) may be better alternatives. The review offers a synthesis of past and contemporary methods used in the analysis of hydroclimatic extremes, aiming to highlight their strengths and weaknesses. Finally, the comparative studies summary helps the reader identify the most suitable modeling framework for their analyses, based on the extreme hydroclimatic variables, sample sizes, locations, and evaluation metrics reviewed.en1879-27072021ElsevierExtremes | Non-stationarity | Precipitation | Probabilistic parameter estimation | Streamflow | TemperatureTechnology::600: TechnologyReview Article10.1016/j.jhydrol.2021.127302Review Article