资源环境科技发展态势分析平台

We develop a hybrid statistical forecasting model for the simultaneous season-ahead forecasting of both seasonal rainfall and the 24-hr maximum rainfall for the upcoming season, using predictors identified through the Shared Reciprocal Nearest Neighbor approach. The model uses a generalized linear regression and a four-parameter Beta distribution model for downscaling extremes using the predictors that were identified. A cross-validation experiment for the last four decades in both Han-River and Geum-River watersheds, South Korea was performed to test the efficacy of the model. The leave-one-out cross-validated seasonal precipitation forecast demonstrates a correlation that ranges from 0.69-0.78 to 0.68-0.76 for the Han-River and Geum-River watershed, respectively. Similarly, for the 24-hr maximum rainfalls in the upcoming season, the cross-validated correlations between the predicted and the observed values range from 0.67-0.73 to 0.50- 0.63, for the two river basins. A discussion of the potential causes of the skill is offered.