Ground-based microwave radiometers (GMWRs) and Mie–Raman lidars (MRLs) provide valuable thermodynamic observations for atmospheric profiling, but conventional variational retrieval frameworks typically rely on static observation weighting assumptions that may not adequately represent varying observation quality under precipitation conditions. To address this limitation, an adaptive observation weighting framework based on the Thermodynamic-Constrained Kalman Filter 1D-Var framework (TCKF1D-Var) is developed and evaluated using 107 nocturnal heavy-precipitation cases. The proposed method dynamically estimates observational contributions during the retrieval process and is applied to GMWR, MRL, and GMWR–MRL synergistic retrievals. Retrieval performance is assessed against radiosonde observations and compared with that of a conventional static-weighting TCKF1D-Var framework. Results show that the adaptive weighting approach consistently improves retrieval accuracy, with the largest benefits found for water vapor mass mixing ratio profiles. For both GMWR and MRL retrievals, reductions in mean bias and root-mean-square error are obtained relative to the static-weighting framework. The synergistic retrieval further improves moisture-profile retrievals and generally achieves the best overall performance among all experiments. Diagnostic analyses reveal that the adaptive framework dynamically adjusts the utilization of observational information according to sensor characteristics and atmospheric conditions, while redistributing observational influence between GMWR and MRL measurements during synergistic retrievals. These results demonstrate that adaptive observation weighting provides an effective strategy for improving thermodynamic profile retrievals under heavy-precipitation pre-onset conditions.