Abstract: To improve the accuracy of 0–2 h precipitation nowcasting over complex terrain, a kilometer- and minute-scale optical-flow fusion framework (Fused) driven by both physics and data principles is proposed, which integrates the Recurrent All-Pairs Field Transforms (RAFT) optical flow with the classical Farneback dense optical flow. This method performs a weighted fusion of the RAFT data-driven optical flow field and the Farneback dense optical flow field, employs a four-step fourth-order Runge-Kutta (RK4) integration scheme for high-accuracy advection extrapolation, and constructs a residual AR(2) model with Kalman error feedback to suppress intensity drift, thereby generating 10-minute-interval extrapolated precipitation fields. The model is trained on the 5-km multi-source merged precipitation analysis product over China from May to September during 2022–2024, and independently validated against 45 precipitation events in 2025 over the Dabie Mountain region, with evaluations conducted from the perspectives of intensity, spatial structure, and object morphology. Verification results show that, within the 120 min forecast lead time, the Fused method achieves an RMSE of 0.55 mm, representing reductions of 19% and 34% compared with the RAFT and Farneback methods, respectively; the rain-area correlation coefficient reaches 0.51. The object-based diagnostic evaluation reveals an object similarity of 0.73, area expansion below 10%, and centroid displacement less than 5 km. The critical success index (CSI) for 1-h rain areas reaches 0.42, while the 2-h forecast remains operationally usable. The mean absolute divergence of the fused optical flow field is 2.8×10⁻⁵ s⁻¹, with a relative error below 5%, satisfying the mass conservation approximation, which ensures the physical rationality of intensity evolution and the reliability of the nowcast.