Abstract
The incremental dynamical downscaling and analysis system (InDDAS) which has been developed from the pseudo-global-warming method by appending partial functions was applied for a probabilistic regional scale climate change projection with the target regions of Kanto and Japan Alps. In InDDAS, the most reliable future state was projected by a regional climate model (RCM) simulation with an ensemble mean among the climatological increments of multiple general circulation model (GCM) simulations. In addition, the uncertainty of the future projections is estimated by RCM simulations with the multi-modal statistical increments calculated by the singular vector decomposition of the multiple GCMs. An increase of rainfall with the change ratio of 7-16 % was projected in Kanto region, where the most reliable value was 10 %. The change ratios of the vicinity quantiles of extreme rainfall was projected to be larger than that of rainfall and was almost the same as the value explained by the Clausius—Clapeyron effect.
