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Different downscaling approaches have been developed for linking daily climate projections at coarse-grid global or regional scales given by Global Climate Models (GCMs) or Regional Climate Models (RCMs) and observed extreme rainfalls at finer time scales for a given location or over an urban catchment for various impact and adaptation studies. In general, these downscaling methods can be grouped into two main categories: dynamical downscaling (DD) and statistical downscaling (SD) techniques. Furthermore, it has been widely known that DD methods could provide reasonable description of the climate conditions for large regional scales, but they could not accurately capture observed characteristics of hydrologic processes such as precipitation at a local or station scale; while SD procedures have been found to be able to describe accurately the observed properties of the local hydrologic processes. Therefore, the overall objective of the present paper is to provide an overview of some recent advances and existing shortcomings in these downscaling approaches for modeling of extreme precipitation processes in a changing climate. In particular, the main focus of this paper is a review of recently developed statistical downscaling methods for linking GCM climate predictors to the observed daily and sub-daily rainfall time series at a given location or at many different sites concurrently over a given urban catchment. Illustrative applications using NCEP re-analysis dataset, climate simulations from different GCMs, and rainfall data available from raingage networks located in Canada are presented to indicate the feasibility and limitations of the proposed statistical downscaling methods for climate change impact and adaptation studies. |
