由於氣候異常現象發生的頻率逐年增加，氣候變遷在近年來成為全球熱門的議題之一。GCMs模式是現今用來客觀分析未來氣候變遷和季節預報的主要工具，並以物理的方程為基礎建立長時間全球氣候系統的模擬，但是在網格解析度太粗的情況下，對中小尺度的研究上有一定的困難，因此為了克服GCMs在模擬區域氣候上的不足，降尺度方法的運用就成為研究區域氣候的必要手段。
本研究主要採用的模式資料為IPCC AR4的二十二個模式資料在20c3m和A1B排放情境與控制情境三種，觀測資料則分別使用Aphrodite的降雨以及CRU的地表溫度，在對歷史氣候進行降尺度方法評估時的時間長度取1961年到2000年共40年；未來推估的應用上，時間長度則取2001年到2099年共99年，水平解析度均內插處理為2˚X2˚。針對陸地上的降雨以及地表溫度進行分析，利用歷史的模式和觀測資料評估統計降尺度方法的成效，再延伸應用到未來A1B情境，探討東亞地區(60˚E~150˚E，0˚N~55˚N)和台灣地區(118˚E~124˚E，19˚N~27˚N)在未來二十年(2080年到2099年)降雨和溫度與過去二十年(1980年到1999年)相比，受到氣候變遷的影響程度以及不確定性的範圍。
在評估歷史氣候的降尺度模擬效用時，可以得知氣候模式對於大尺度的氣候特徵以及變化趨勢能夠有效的掌握到，但是縮小範圍至局部地區，空間上的分布變化卻明顯的模擬能力不足，無法為區域研究提供充分且有效的資訊，而降尺度方法能夠提升空間解析度提供更多資訊、有效修正模式模擬的誤差、呈現局部地區受海陸地形分布的影響以及從環流模式擷取模式中有用的資訊等。因此對於東亞局部區域的台灣而言，降尺度方法在研究上就佔有相當重要的角色。
在檢驗的部分，從統計上透過Monte Carlo和T-test等方法對資料進行物差以及不確定性的驗證，使用Monte Carlo檢定的目的在於濾除因降尺度方法所產生的誤差，並保留通過90%信賴水準的氣候變化量；使用T-test檢定的目的在於濾除模式內包含的自然變動所產生的非氣候變遷訊號，並保留通過95%雙尾檢定(T=1.9935)的網格位置。
從研究結果顯示，東亞地區的降雨變化趨勢整體是呈現增加的情況，而且冬季增加的幅度大於年平均大於夏季，但也有部分地區出現減少的訊號，例如夏季的中亞地區和冬季的低緯度地區，顯示降雨隨空間變化的差異大。在經過檢定之後，降雨增加的幅度都有所提升，通過檢定的變化趨勢也相對較為集中；在地表溫度上整體都是增溫的趨勢，而且增溫的幅度有隨緯度遞增的情形，年平均增溫大於冬季大於夏季。在檢定前後的模式數量的差異不大，代表溫度的變化都是具有統計顯著性與高可信度。
若有更好的誤差修正方法與解析度更高的觀測資料，配合適當的統計檢定方式，就能更進一步改善降尺度方法對未來氣候的評估結果。

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