Intensity-duration-frequency (IDF) curves for extreme precipitation events are widely used to design civil infrastructures like sewers and dikes. However, IDF curves are sometimes available only in sparse locations. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially suited for the covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over the Eastern part of Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice which consists in taking the IDF curve from the closest available station.
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Christian Genest is a professor in the Department of Mathematics and Statistics at McGill University (Montréal, Canada), where he holds a Canada Research Chair. He is the author of numerous research papers in multivariate analysis, nonparametric statistics, extreme-value theory, and multiple-criteria decision analysis. He is elected member of ISI, Fellow of ASA, ISM and Royal Society of Canada, and received a Humboldt Research Prize in 2019. He has served the mathematical and statistical comunities in distinct ways. Was president of the Statistial Association of Canada and ASSQ in Quebec, and editor and associate editor of pretigious journals in statistics.