Extreme rainfall events often lead to excessive river flows and severe flooding for Pacific Island nations. Fiji, in particular, is often exposed to extreme rainfall events and associated flooding, with significant impacts on properties, infrastructure, agriculture, and the tourism sector. While these occurrences are often associated with tropical cyclones (TCs), the specific characteristics of TCs that produce extreme rainfall are not well understood. In particular, TC intensity does not appear to be a useful guide in predicting rainfall, since weaker TCs are capable of producing large rainfall compared to more intense systems. Therefore, other TC characteristics, in particular TC track morphology and background climate conditions, may provide more useful insights into what drives TC related extreme rainfall. This study aimed to address this problem by developing a decision tree to identify the most important predictors of TC related extreme rainfall (i.e., 95^th percentile) for Fiji. TC attributes considered include; TC duration, the average moving speed of TCs, the minimum distance of TCs from land, seasonality, intensity (wind speed) and the geometry of TCs (i.e., geographical location, shape and length via cluster and sinuosity analyses of TC tracks). In addition, potential predictors based on the phases of Indo-Pacific climate modes were input to the decision tree to represent large scale background conditions. It was found that a TC's minimum distance from land was the most important influence on extreme rainfall, followed by TC cluster grouping, seasonality and duration. The application of this model could result in improved TC risk evaluations and could be used by forecasters and decision-makers on mitigating TC impacts over the Fiji Islands.

极端降雨事件通常会导致太平洋岛国河流流量过大和严重洪灾。尤其是斐济，经常遭受极端降雨事件和相关洪水的侵袭，对财产、基础设施、农业和旅游业产生重大影响。虽然这些事件通常与热带气旋 (TC) 相关，但产生极端降雨的 TC 的具体特征尚不清楚。特别是，TC 强度似乎不是预测降雨的有用指南，因为与强度更高的系统相比，较弱的 TC 能够产生大量降雨。因此，其他 TC 特征，特别是 TC 轨迹形态和背景气候条件，可能会提供更有用的见解，以了解 TC 相关极端降雨的驱动因素。即，^第95^个百分位数）斐济。考虑的 TC 属性包括；TC 持续时间、TC 的平均移动速度、TC 离陆地的最小距离、季节性、强度（风速）和 TC 的几何形状（即，通过 TC 轨迹的​​聚类和曲折度分析的地理位置、形状和长度）。此外，基于印度洋-太平洋气候模式阶段的潜在预测因子被输入到决策树中，以表示大尺度背景条件。结果表明，台风距陆地最小距离对极端降雨影响最大，其次是台风集群分组、季节性和持续时间。该模型的应用可改进 TC 风险评估，并可被预报员和决策者用于减轻 TC 对斐济群岛的影响。