Commonsense inference and domain-specific expertise are crucial for understanding and responding to emotional, cognitive, and topic-specific cues in counseling conversations with crime victims. However, these key evidences are often dispersed across multiple utterances, making it difficult to capture through single-hop reasoning. To address this, we propose MENDER, a novel Multi-hop commonsensE and domaiN-specific Chain-of-Thought (CoT) reasoning framework for knowleDge-grounded empathEtic Response generation in counseling dialogues. MENDER leverages large language models (LLMs) to integrate commonsense and domain knowledge via multi-hop reasoning over the dialogue context. It employs two specialized reasoning chains, viz. Commonsense Knowledge-driven CoT and Domain Knowledge-driven CoT rationales, which extract and aggregate dispersed emotional, cognitive, and topical evidences to generate knowledge-grounded empathetic counseling responses. Experimental evaluations on counseling dialogue dataset, POEM validate MENDER’s efficacy in generating coherent, empathetic, knowledge-grounded responses.