IDENTIFYING GAPS AND OPPORTUNITIES BETWEEN STATISTICAL AND DYNAMICAL DOWNSCALING APPROACHES OVER SHIKOKU ISLAND, JAPAN

Abstract

Downscaling plays a major role in evaluating future climate change and setting up appropriate adaptation strategies at regional and local scale, in which the effects of climate change will mostly be felt. The objective of this study was to evaluate statistical bias correction and Dynamical Downscaling (DD) approaches by applying them to present climate of rainfall over Shikoku Island, Japan. DD captured well spatial and temporal distributions owing to the direct influence of the spatial resolution enhancement. It has been found that statistical bias correction has limitations in estimating extreme heavy rainfall intensity as well as frequency with seasonal consistency. In particular, all selected GCMs completely failed to predict the extremely heavy rainfall events in July that cannot be rectified by statistical method. As DD method has produced consistent results with observation, the mismatching frequency and intensity with seasons can be eliminating by performing Pseudo Global Warming Downscale (PGW-DS) and identified systematic biases in dynamical downscaling can be complimented by statistical approach. This proposed method aims to provide better science-based information on climate change.