Multi-resolution models can be statically (i.e., before simulation) or dynamically (i.e., during simulation) abstracted to accelerate the simulations without compromising the analysis goals. However, abstractions must be carefully chosen because not all abstractions improve performance. Unfortunately, identifying performance enhancing transformations, particularly in parallel simulations, is a complex task. We have been investigating this bottleneck using a component-based, Time Warp synchronized modeling and simulation environment called WESE in which static and dynamic abstractions are performed using a methodology called Dynamic Component Substitution (DCS). Our ongoing research has resulted in further advances to a novel DCS Performance Prediction Methodology (DCSPPM). DCSPPM combines and compares platform-specific performance characteristics of components via static analysis of the model to predict performance changes due to DCS transformations. This paper presents the enhanced version of DCSPPM and experiments. Empirical evidence obtained using diverse models indicates that DCSPPM provides good estimates (error \le 5%) of the performance impacts of DCS transformations.