Landslide deformation is the most intuitive and effective characterization of the evolution of landslides and reveals their inherent risk. Considering the inadequacy of existing deformation monitoring data in the early warning of landslide hazards, resulting in insufficient disaster response times, this paper proposes a time-domain correlation model. Based on a regional rainfall-landslide deformation response analysis method, a time-domain correlation measure between regional rainfall and landslide deformation and a calculation method based on impulse response functions are proposed for prevalent rainfall-induced landslide areas, and the correlation with the rainfall-triggered landslide deformation mechanism is quantitatively modeled. Furthermore, using rainfall monitoring data to optimize the indicator system for landslide deformation monitoring and early warning significantly improves the preliminary warning based on landslide deformation. The feasibility of the method proposed in this paper is verified by analyzing the historical monitoring data of rainfall and landslide deformation at 15 typical locations in 5 landslide hidden hazard areas in Fengjie County, Chongqing city. (1) The correlation models for the XP landslide and XSP landslide involve a 5-day lagged correlation under a 56-day cycle and a 18-21-day lagged correlation under a 49-52-day cycle, which means that the deformation in the above areas can be modeled cyclically according to monitoring data, and early landslide warnings can be provided in advance with a lag time. (2) The correlation models for the TMS landslide and OT landslide show consistent correlations under a 48-50-day cycle and a 58-day cycle, which means that the deformation in the above areas can be predicted based on rainfall accumulation, and real-time warnings of future landslide deformation and displacement can be obtained. (3) The HJWC landslide presents a disorderly correlation pattern, which means that a preliminary landslide deformation warning cannot be provided based on rainfall alone; other monitoring data need to be supplemented and analyzed.