本研究利用長期高解析度逐日觀測資料，嘗試建立極端降雨指標的空間差異變量，並用此變量對IPCC AR4(2007)的氣候模式資料進行降尺度，以提升GCMs針對區域極端降雨的強度及空間分布描述。為了降低氣候模式本身因物理方法及參數設定對於極端降雨模擬的誤差，本文應用Wood(2004)發展的誤差修正方法，將觀測資料排序成累積機率分布(CDF)曲線，利用此曲線為基準，對模式歷史模擬及未來模擬作整體強度範圍的校正，以消除模式本身的誤差。
透過統計降尺度及誤差修正方法，本文探討了下列四種極端降雨指標： 1.年最大單日累積降雨量(RX1DAY)；2.年最大五日累積降雨量(RX5DAY)；3.年期間雨日日數(RR1)，以及4.年平均雨日降雨強度(SDII)。
研究結果顯示，此方法對極端降雨在氣候變遷的區域特徵及強度推估上均有顯著描述。(1)季風亞洲地區：可將氣候模式解析度提升至0.25度X0.25度，在原始解析度下強度不足的大陸東南沿海區域及孟加拉灣北部極端降雨特徵都有明顯的改善。(2)台灣地區：透過本研究方法提升解析度和強度特徵後，可顯示出北部、中部，及南部之極端降雨特徵。另外利用此方法將目前推估未來氣候變遷的低解析度氣候模式，可得到高解析度極端氣候事件長期變遷的推估，而且可以保留多個氣候模式推估結果，以呈現多個氣候模式的不確定性範圍。

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