Seasonal Prediction of Monthly Precipitation in China Using Large-Scale Climate Indices

In this study, seasonal predictions were applied to precipitation in China on a monthly basis based on a multivariate linear regression with an adaptive choice of predictors drawn from regularly updated climate indices with a two to twelve month lead time. A leave-one-out cross validation was applied to obtain hindcast skill at a 1% significance level. The skill of forecast models at a monthly scale and their significance levels were evaluated using Anomaly Correlation Coefficients (ACC) and Coefficients Of Determination (COD). The monthly ACC skill ranged between 0.43 and 0.50 in Central China, 0.41--0.57 in East China, and 0.41--0.60 in South China. The dynamic link between large-scale climate indices with lead time and the precipitation in China is also discussed based on Singular Value Decomposition Analysis (SVDA) and Correlation Analysis (CA).