A smooth copula-based Generalized Extreme Value (GEV) model will be presented, using a two-steps approach combining GEV parameters' smooth functions in space through the use of spatial covariates and a flexible hierarchical copula-based model to take into account dependency between the locations. The hierarchical copula structure is detected via a clustering algorithm implemented with an adapted version of the copula-based dissimilarity measure recently introduced in the literature. The considered data contains a large portion of missing values, and one observes several non-concomitant record periods at different locations. We compare the classical GEV parameter interpolation approaches with the proposed smooth copula-based GEV modeling approach, and apply the model to extreme rainfall in eastern Canada.