Seasonal precipitation prediction via data-adaptive principal component regression

Abstract

This article studies a problem of predicting seasonal precipitation over East Asia from real observations and multi-model ensembles. Classical model output statistics approach based on principal component analysis (PCA) has been widely used for climate prediction. However, it may not be efficient in predicting precipitation since PCA assumes that information of data should be retained by the second moment of them, which is too stringent to climate data that can be skewed or asymmetric. This article presents a method based on data-adaptive PCA (DPCA) by Lim and Oh (2016) that can adapt to non-Gaussian distributed data. In addition to investigate the utility of DPCA for climate study, we propose a data-adaptive principal component regression for seasonal precipitation prediction, which consists of DPCA and a regularized regression technique that is able to handle high-dimensional data. We apply the proposed method to nine general circulation models for prediction of precipitations on the summer season (June, July, and August). The prediction ability of the proposed method is evaluated in comparison with observations and model outputs (prediction) of each constituent model.