Climate Prediction by a Hybrid Method with Emphasizing Future Precipitation Change of East Asia Lim, Yae-Ji; Jo, Seong-Il; Lee, Jae-Yong; Oh, Hee-Seok; Kang, Hyun-Suk;
A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.