極端天氣或氣候頻率和強度的改變對自然環境和人類社會有顯著的影響。聯合國跨政府氣候變遷小組（Intergovernmental Panel on Climate Change)第四次報告中提出，對未來氣候變遷的情境推估結果，極端降雨事件的發生頻率在大多數的地區都有增加的趨勢。這些未來的預測主要是建立在氣候模式模擬極端降雨分布的分析結果，但上述推估最受爭論的地方，在於低解析度的模式模擬，往往無法正確呈現需要高時空解析度的極端天氣現象，而這對模式模擬未來極端天氣變化的可靠性就有所質疑。而解決這問題的途徑之一，是運用超高解析度的區域或全球氣候模式，這需要花去相當多的運算成本與資料儲存資源，只有極少數的氣候研究中心才能做到，但是在這種情形下，反而失去了多個氣候模式所能呈現的氣候變遷情境推估不確定性範圍。
近年來，有些運用測站或衛星觀測所整理的網格化降雨分析資料，已經能提供較高解析度和較長的涵蓋時間範圍，這些資料的時間長度足夠提供較多的極端天氣抽樣。我們可以通過統計方法瞭解觀測資料在不同尺度上的連結，並應用於極端天氣或氣候指標的降尺度方法上。利用此方法在現階段推估未來氣候變遷的低解析度氣候模式上，便可以得到極端天氣事件長期變遷的高解析度推估，並同時兼顧多個氣候模式所能呈現的不確定性範圍。

Changes in the frequency or intensify of extreme weather and climate events could have profound impacts on both human society and the natural environment. The IPCC 4th assessment concludes that frequency (or proportion of total rainfall from heavy falls) of heavy precipitation events are very likely to increase over most areas as the climate warms. These future projections are mainly reply on the simulation of extreme rainfall distribution in the current generation of climate model. It is often argued that relatively low resolution climate model can’t properly reproduced the high-impact weather extremes. This raises issues on the reliability of their future projections on extremes. In response to the question, very high resolution version of climate models run under the time-slices experiment design or fine-scaled regional climate models forced by global model result from lateral boundaries are used to explore the problem. Although it generally matched better with station rainfall data or high-resolution gridded observational analysis, the cost of such high resolution model runs are excessive to be affordable to create multi-models and multiple-member ensembles that better sample the uncertainty in future projections.
Recently high temporal and spatial resolution ground station analysis and satellite estimates are available for climate study. The length of data record are starting to provide enough sampling on the extreme weather events. It is well know that there is spatial scaling issue concerning on the study on the extreme weather events. By studying statistical properties that link the different spatial scale in the observational data. One can develop statistical downscaling method for the extreme weather and climate indices. Applying the methodology to the CMIP3 climate models, it is possible to derive very high resolution extreme statistics based on observational relationship. The result should be welcomed by the community working on the impact and adaptation study that need more local projection on the extreme events.

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