Spatial-temporal graph neural networks have demonstrated remarkable performance in time series forecasting. However, their inherent opacity poses a significant challenge, limiting their adoption in high-stakes applications. Existing post-hoc explainers are fundamentally limited, as they operate on static physical topologies and isolated time slices, failing to capture essential semantic relationships and long-range temporal dynamics. To address this, we propose FusionSHAP, a novel model-agnostic explanation framework. FusionSHAP makes two primary contributions. First, it introduces Semantic-Physical Graph Fusion, a process that constructs a hybrid graph by augmenting the conventional physical topology with a semantic graph generated by a Large Language Model. This fusion enables the discovery of non-obvious, functionally-driven relationships between nodes. Second, the framework employs Window-Level Attribution with Causal Lag Alignment, a method that adapts KernelSHAP to a node-timestep feature space. This provides temporally-precise explanations that pinpoint not only which nodes were influential but also when. Experiments on real-world traffic forecasting datasets demonstrate that FusionSHAP generates explanations with demonstrably higher fidelity and sparsity than existing methods, paving a new path toward uncovering the complex, non-local, and temporally-specific dependencies learned by STGNNs.