Accurate characterization of boundary-layer water vapor prior to nocturnal heavy precipitation remains challenging due to limited observational capability. In this study, we build upon a previously developed and validated thermodynamic- and cloud-microphysics-constrained Kalman filter one-dimensional variational (TCKF1D-Var) framework by extending it to incorporate nitrogen and water vapor Raman channel observations from the China Meteorological Administration Mie–Raman lidar (MRL) network. A physics-informed lidar observation operator based on the classical Raman lidar formulation is developed, together with a data-driven calibration component to account for time-varying instrumental and aerosol-related uncertainties. In addition, process and observation error covariance matrices are dynamically estimated within the Kalman filter framework to enhance retrieval robustness. The method is evaluated against co-located radiosonde observations launched prior to nocturnal heavy precipitation events at 56 MRL–radiosonde co-located stations across China in 2025. The retrieved water vapor mass mixing ratio profiles, with a vertical resolution of 30 meters and a temporal resolution of 30 minutes, exhibit consistently reduced mean bias and root mean square error compared to ERA5 prior profiles, with the largest improvements found in the 1.2–3.0 km layer. Analysis of nocturnal heavy precipitation cases further demonstrates that the retrievals capture coherent pre-precipitation moisture evolution. These results highlight the potential of combining physically constrained retrieval frameworks with Raman lidar observations for improved monitoring of boundary-layer moisture.