<p><?tk 4?>Flooding events caused by rainfall can lead to road waterlogging and metro station closures, significantly disrupting the operation of urban multimodal transportation networks. An urban multimodal transportation network integrating cars, buses, and metro systems is constructed using multi-source data and analyzed through complex network theory. A cascading failure algorithm is applied to simulate passenger flow under different rainfall intensity scenarios. To assess network resilience, an overall resilience index is developed that incorporates absorption efficiency, mobility, and recoverability. The Ward hierarchical clustering algorithm is employed to identify critical edges influencing network resilience. Strategies for enhancing the flood resilience of these critical edges are also proposed. This study yields several key findings regarding the resilience of urban multimodal transportation networks under varying rainfall intensities. Under heavy rainfall, the network maintains a resilience score of 0.895, indicating that passenger movement remains largely unaffected within the network. However, during torrential rain conditions, the resilience score drops to 0.593, with most edges experiencing congestion. In such scenarios, critical edges across the car, bus, and metro sub-networks are found to play a vital role in preserving overall network functionality. Furthermore, with the optimal capacity improvement factor, the overall network resilience increases by 6.7%. These findings contribute to the theoretical understanding and practical enhancement of urban transportation resilience in the context of rainfall-induced disruptions.</p>