近年來，台灣屢屢受到缺水影響之苦，各地皆實施限水的措施，一旦水源供應不足，對於農業、工業，甚或是高科技產業之發展都會產生嚴重的影響與妨礙，因此，徹底的改善方法除了加強節約用水的觀念以外，若是我們能藉由世界海水表面溫度的變化情形對台灣附近的降雨做較佳的預測，如此便可以配合雨季和旱季來調節儲水與用水，或許能夠對現今台灣的缺水情況有所改善。本篇論文主要目的為藉由海水表面溫度的資料預測台灣附近夏季雨量，我們首先檢定海水表面溫度的變化是否對台灣附近的夏季雨量具有影響性，再以海水表面溫度的資料有效的預測降雨量，其預測能力優於傳統的時序模型。

In this work, a regression model is proposed for predicting the summer rainfall around Taiwan area in which the global sea surface temperature (SST) are incorporated as the predictive variables. In constructing such a regression model, we first identify the effective spatial area according to the Portmanteau test statistics which quantify the dependence between the rainfall series and the SST series for each location. Then, the SST data within the identified area are summarized by the most important principal components to reduce the dimension of the predictive variables. Finally, the regression model is built by regressing the rainfall on these principal components of past SST data. The empirical results show that the regression model incorporating the information of SST performs better than the conventionally
time series model in terms of smaller prediction mean square errors.

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