臺灣的颱風降雨雖為主要的水源之一，但也常因此造成災害。數值天氣預報開發以來，時常面臨著不確定性造成的困難，眾多研究者嘗試使用各式方法獲取防災資訊，本研究面對臺灣颱風防災的需求，嘗試在現有資源限制下，建構針對臺灣颱風降水於防災的建議系統。
具體而言，本研究使用雲解析風暴模式，建構上採用了2.5km水平格點間距，每六小時進行八天的預報，在本研究的十個目標颱風內，經過評估後皆能在各颱風風雨影響臺灣前至少52小時前，也就是大約兩天以前，找出颱風影響期間總累積降雨量的相似技術得分(Similarity Skill Score，簡稱SSS) 大於0.6的高解析度的降水情境，顯示此方法實際應用上可以在有反應時間的前提下，提供有效的降水情境。
防災事前可以針對最糟糕的降水情境做防範，但為了更有效的找出較有參考價值的預報，本研究針對十個西行侵臺颱風選出105個預報參數，使用機器學習嘗試建構能夠預估降水指引(SSS為其中之一)與路徑指引的模型並進行評估，評估後大多數機器學習預估的SSS皆能適度掌握不同初始時間預報中的實際降水SSS好壞。機器學習預估的結果約在實際颱風影響臺灣26小時前可以產出，當預估的SSS中位數達0.6以上時，實際的SSS也有71%超過0.6，顯示可以在中心登陸兩天前預先指出那些預報的可信度低，那些的可信度高。
本研究進行機器學習訓練時進入模型的颱風數量上不多，可當作ㄧ初步之研究，文章中亦討論了許多颱風預報相關性分析，並提出了幾個可能的改進方向。總體來說，高解析差時系集預報輔以機器學習可在臺灣西行颱風的防災預警上，提供有效的降水情境，並指出較具可信度的預報。

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