A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes
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To study trends in extreme precipitation across US over the years 1951-2017, we consider 10 climate indexes that represent extreme precipitation, such as annual maximum of daily precipitation, annual maximum of consecutive 5-day average precipitation, which exhibit spatial correlation as well as mutual dependence. We consider the gridded data, produced by the CLIMDEX project (http://www.climdex.org/gewocs.html), constructed using daily precipitation data. In this paper, we propose a multivariate spatial skew-t process for joint modeling of extreme precipitation indexes and discuss its theoretical properties. The model framework allows Bayesian inference while maintaining a computational time that is competitive with common multivariate geostatistical approaches. In a numerical study, we find that the proposed model outperforms multivariate spatial Gaussian processes, multivariate spatial t-processes including their univariate alternatives in terms of various model selection criteria. We apply the proposed model to estimate the average decadal change in the extreme precipitation indexes throughout the United States and find several significant local changes.
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