## 5 Conclusions  
The continuous intensification of global climate change and urbanization has led to frequent extreme events, including rising sea levels, strong typhoons, heavy rainfall, and extreme runoff, shortening the recurrence period of once-in-a-century flood disasters in coastal areas. The complex interaction between multiple disaster-causing factors under climate change, along with exposure and vulnerability of affected regions, makes studying compound flood risks in coastal areas crucial for global disaster reduction strategies. This study first identified methods for calculating the quantitative impact of climate change factors such as sea level rise, storm surges, and extreme rainfall on compound floods. Next, the influence of urbanization factors, including land subsidence, impervious surfaces, and drainage systems was analyzed. On this basis, three quantitative methods for assessing the impact of climate change on compound floods in coastal cities were discussed: statistical models, numerical models, and machine learning models. Each method reflects its advantages and disadvantages: statistical models require a sufficient sample size for accuracy; numerical models are complex and time-intensive; and machine learning models rely heavily on large amounts of high-quality training data, requiring careful selection of the optimal algorithm for best performance. Although this study makes valuable contributions, it has certain limitations. First, the study only considered data from the WOS Core Collection, with the language restricted to English, which may have led to the omission of valuable research from other databases and non-English sources. Therefore, it is necessary to consider data from multiple databases and a variety of languages for a more comprehensive review. Second, existing research largely focuses on the compound effects of two primary drivers (e.g., storm surge and rainfall), while studies examining the simultaneous interaction of three or more drivers are relatively scarce. As a result, this review may not have fully captured more complex scenarios involving multiple interacting drivers. Research on compound floods under future climate change can be further advanced in the following areas. Firstly, due to the challenges in constructing three-dimensional joint probability statistical models, their complex structure, high computational complexity, and the increasing number of flood drivers leading to a rise in the number of numerical models used, coupled with complex grid interfaces and frameworks between different models, most existing research on compound floods under climate change is based on the superposition of two disaster-causing factors. Research on compound floods involving more than three disaster-causing factors is relatively limited. To better assess the impact and future trends of climate change on extreme rainfall, runoff, storm surges, and compound flood events, simultaneous calculation and evaluation of multiple factors such as sea level rise, direct surface precipitation, storm surge flooding, and river floods should be prioritized. Secondly, previous research has focused on the impact of different meteorological factors on floods, while neglecting the changes in natural underlying surface conditions caused by urbanization. Urbanization can lead to abrupt changes in precipitation process characteristics, and severe ground subsidence may further intensify compound flood risks. Future studies should incorporate coastal urbanization factors into flood models and enhance analyses of how land subsidence, impervious surfaces, and limited drainage system capacity influence responses to compound floods. Thirdly, statistical models can provide insights into the spatiotemporal changes of compound floods on a large scale and their relationship with climate factors. Numerical models can more accurately depict the evolution process of compound floods.