Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Extreme weather events will become more frequent and severe as a result of climate change, necessitating an immediate need for cities to adapt to future climate change. Therefore, the prediction of future precipitation and waterlogging is of utmost importance. Using Beijing as an example, the simulation capability of different models was evaluated, and the optimal model for the study area was screened using Taylor diagrams and interannual variability scores, along with actual monthly precipitation data from Chinese weather stations from 1994 to 2014 and historical monthly precipitation data from 10 coupled models from Coupled Model Intercomparison Project Phase 6 (CMIP6). The SWMM model was then used to simulate future rainfall and waterlogging scenarios for the study area using precipitation forecast data for 2020–2050 from the best model to investigate the impact of climate change on future rainfall and waterlogging in urban areas. CMIP6 brings together the most recent simulation data from major climate models on a global scale, providing a broader and more diverse range of model results and thereby making future predictions more accurate and dependable, and its findings provide a theoretical foundation for the emergency management of and scientific responses to urban flooding events. The following major conclusions were reached: 1. The best-performing models are EC-Earth3, GFDL-ESM4, and MPI- ESM1-2-HR. EC-Earth3 is a modular Earth system model developed collaboratively by a European consortium. MPI-ESM1-2 is a climate precipitation prediction model developed in Germany and promoted for global application, whereas the GFDL-ESM4 model was developed in the United States and is currently employed for global climate precipitation simulations. 2. Under future climate circumstances, the total annual precipitation in the example region simulated by all three models increases by a maximum of 40%. 3. Under future climatic conditions, urban surface runoff and nodal overflow in the study area will be more significant. The node overflow will become more severe with the increase in climate scenario oppression, and the potential overflow nodes will account for 1.5%, 2.7%, and 2.9% of the total number of nodes under the SSP1–2.6, SSP2–4.5, and SSP5–8.5 scenarios, respectively. 4. In the future, the effectiveness of stormwater drainage systems may diminish. To increase climate change resilience, the impacts of climate change should be considered when planning the scope of stormwater optimization and the integrated improvement of gray–green–blue facilities.

Evaluating waterlogging vulnerability and analyzing its characteristics and future trends can provide scientific support for urban disaster prevention and reduction. For this study, taking Fuzhou as an example, an urban waterlogging vulnerability assessment system was constructed from the three dimensions of exposure, sensitivity, and adaptive capacity. The entropy method was used to evaluate urban waterlogging vulnerability in Fuzhou during 2014–2020. The use of CA–Markov to predict waterlogging vulnerability in 2023, 2026, and 2029 in Fuzhou is an important innovation reported in this paper. Study results showed that: (1) Vulnerability to waterlogging in Fuzhou follows a gradually decreasing “center-southeast” distribution pattern, with Level 5 areas mainly located in Cangshan District, Gulou District, and Taijiang District. (2) Changes in waterlogging vulnerability in Fuzhou from 2014 to 2020 can be divided into five change modes, with changing areas, mainly of the late-change type, accounting for 14.13% of the total area. (3) Prediction accuracy verification shows that the CA–Markov model is suitable for predicting waterlogging vulnerability in Fuzhou with high accuracy and a kappa coefficient of 0.9079. (4) From 2020 to 2029, the vulnerability level of the eastern coastal region of Fuzhou is expected to generally increase, and the vulnerability degree will continue to deteriorate. The proportion of Level 5 vulnerable areas will increase by 4.5%, and the growth rate will increase faster and faster with the passage of time.

Due to the increase in frequency and severity, assessing and predicting urban waterlogging risk is critical. The risk assessment framework is based on three factors: hazard, exposure, and vulnerability. The assessment indicators, previously based solely on static indicators, account for the effects of varying temporal and spatial distributions of people and vehicles on the assessment results. Specifically, two dynamic indicators—the population density and the Traffic Performance Index (TPI)—are added to the mix to dynamically assess the risk of waterlogging in the central urban area of Suqian City of Jiangsu Province, China’s central urban area, over various periods. The findings indicate that four–six times more individuals are affected during peak hours than during other periods, and no important roads are within the scope of waterlogging during other periods, while nearly ten important roads will be affected during peak hours. Additionally, the characteristics of the temporal and spatial distribution of waterlogging risk can be more accurately represented by a combination of static and dynamic indicators. The highest risk areas are significantly more prominent during the weekday peak period than during other times; the morning peak is mainly affected by traffic performance indicators, the evening peak is mainly affected by population density, and the main factors affecting the other periods are the same as the other main factors affecting the peak period. The highest risk areas are mainly located in the eastern part of the central urban area of Suqian City, with the lowest risk in the north and south.