The Weighted Partial MaxSAT (WPMS) problem requires finding an assignment that satisfies all hard clauses while maximizing the total weight of satisfied soft clauses. From a computational perspective, WPMS is not merely an extension of the uniform-weight Partial MaxSAT (PMS); rather, it requires search strategies that actively exploit weight information to navigate the solution space. While state-of-the-art heuristic and SAT-based solvers have successfully integrated weight-aware mechanisms, Branch-and-Bound (BnB) solvers, notably WMaxCDCL, largely treat weight information passively during search and pruning. In this paper, we bridge this gap by introducing two novel weight-aware strategies into the BnB framework. Our methods integrate weight information directly into the preprocessing and lower-bound estimation. Experimental results demonstrate that these strategies significantly enhance the performance of WMaxCDCL, establishing a new state-of-the-art for exact WPMS solving.