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Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates. [ABSTRACT FROM AUTHOR]

Multivariate frequency analysis has been developed for analyzing hydro-meteorological data such as rainfall, flood, and drought. Particularly, the copula model has been used as a useful tool for multivariate frequency analysis because the copula model has no limitation on deciding marginal distributions. Generally, the inference function for margin (IFM) and the maximum pseudo-likelihood (MPL) are the most common methods to estimate the copula parameter. However, the MPL method includes the non-parametric procedure which cannot consider the skewness of the data and suppress characteristics of the variables. In this study, the modified MPL method is presented by substituting the non-parametric procedure and assessed their performances with the Monte-Carlo simulation. In simulation procedure, the GEV distribution is selected as a population of marginal distribution with positive-skewed parameter set. Then, the estimated parameters from the IFM, MPL and modified MPL methods are compared to true value. [ABSTRACT FROM AUTHOR]