Abstract: Based on rainstorm processes and disaster data from Hebei Province during 1978–2020, critical disaster-causing factors are identified. Moreover, the continuous days of rainfall, process precipitation, and average and maximum daily precipitation are significantly correlated with direct economic loss and affected populations. This method of determining causing factors is more objective and practical. Further, a hazard intensity index is constructed by comparing four weighting methods. The weights obtained using the modified correlation coefficient method are 0.188, 0.312, 0.208, and 0.292, demonstrating more subjective cognition. The topographic and hydrographic factors are used to evaluate the sensitivity of the hazard-formative environment. The hazard in the central region is higher than that in the northern and eastern regions, with Baoding and Xiong’an areas showing the highest hazard levels. Based on whether to activate the flood storage and detention areas, influence classes are judged based on the distance and more comprehensive evaluation factors. By combining the exposure and vulnerability of disaster-affected bodies, an impact assessment of the “23·7” extreme rainfall is conducted. This extreme rainfall process had the highest impact on central Xingtai, central–eastern Baoding, central–western Shijiazhuang, central–northern Langfang, and the junction of Hengshui and Cangzhou. The results of two scenarios are evaluated: One wherein the flood storage area is considered and one wherein it is not considered. The accuracy of the assessment wherein the impact of the flood storage and detention areas is considered as 76.93%, which is 9.41% higher than the assessment wherein these factors are not considered. This method demonstrates strong operability, and the obtained result is more consistent with reality, making it suitable for the impact assessment of extreme rainfall processes in Hebei Province. The proposed method can be applied to impact estimation before extreme rainfall events, track analysis during the events, and perform rapid assessments after the events to improve the pertinence of meteorological services.