Resilience against extreme rainfall and its induced flooding is critical for a truck freight system during extreme events and post-event recovery. This study presents a two-step modeling framework that integrates a flood simulation model and a freight demand model to quantify the resilience of the truck freight system against extreme rainfall events. In the initial step, using rainfall data from meteorological stations, the catchment-based macro-scale floodplain (CaMa-Flood) model was introduced to simulate the rainfall event and its impacts on each road segment’s capacity within the study region. Then, a regional truck freight demand model was built using vehicle trajectory data from heavy-duty trucks operating during the study period to simulate the travel time changes for each affected road segment as a metric to analyze their importance to both functional and topological resilience of the truck freight system. These road segments were ranked based on the travel time increases, with the segment showing the greatest increase ranked as the most critical. To validate the proposed method, an extreme rainfall event in Beijing, Tianjin, and Hebei in July 2023 was modeled. The proposed method can be used to identify key infrastructure improvements to minimize disruptions to the truck freight system, providing decision support for climate-resilient transportation planning essential for achieving UN Sustainable Development Goals (SDG 9 on resilient infrastructure and SDG 13 on climate action).