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Statistical Downscaling (SD) techniques can be used to predict local rainfall data by using the General Circulation Model (GCM) output data as large-scale global data. Previous research concluded that SD techniques in two-stage modeling with classification using monthly rainfall data can reduce errors in one-stage modeling with Partial Least Square Regression (PLSR). In this study, SD techniques in two-stage modeling with classification are used to predict daily rainfall data. First, the robustness of Boosting method in classification was used to determine the occurrence of rainfall in a day. Second, the PLSR method was used to predict amount of rainfall in rainy days predicted by Boosting method. The capability of the model is tested in four stations all located in West Java Province. Results obtained from 5-fold Cross Validation with 2 repeats clearly show that the RMSEP value will be decrease if the classification accuracy value increase.

Daily Rainfall Prediction using Two-Stage Modeling with Boosting Classification on Statistical Downscaling