Graph neural networks (GNNs) have achieved remarkable performance on graph classification tasks, but their black-box nature has raised widespread concerns about the explainability of their prediction process. As a self-explaining mechanism, rationale extraction has received increasing attention in recent years. Its goal is to extract concise subgraph structures from the original graph (i.e., rationale subgraphs) as explanations while generating prediction results. However, existing methods often rely on spurious shortcut features in the data, resulting in explanations that lack faithfulness, which in turn compromises both the interpretability and robustness of the model. To address these issues, this study proposes a complement-aware rationale extraction (CaR) method, which treats the subgraph regions not selected as rationales as complement information. The method enhances counterfactual modeling and interpretability from the following three perspectives. First, a contrastive learning mechanism is introduced to disentangle rationale representations from complement representations, enhancing their semantic independence. Second, an echo-learning strategy is proposed to fully leverage the intermediate representations generated during the message-passing process of GNNs, capturing the structural differences in complement parts across different network depths. Finally, the method combines complement and rationale representations from both current and historical layers to construct counterfactual samples, increasing the diversity of the training data. Extensive experiments on multiple real-world benchmark datasets and a synthetic dataset demonstrate the effectiveness of CaR in producing faithful rationales.