All Times EDT
Keywords: Zero-inflation, GAMLSS, Quantile matching, Time-varying copulas
Traditionally, zero-inflation in daily precipitation was examined in a two-stage approach: one model for the rain occurrence process and the other model for rain amount conditioned on the occurrence of a wet day from the first stage. This study unifies the two processes by using a Generalized Additive Model for Location, Scale, and Shape (GAMLSS) to estimate a full-distributional climatology of daily precipitation over a single location. Then, utilizing corresponding regional climate model information, we develop two statistical downscaling approaches: the first one is rooted in the quantile-matching framework and the other one is based on a novel time-varying Archimedean copulas scheme, they both enable projections of future precipitation patterns based on the selected daily rainfall model.