With the widespread adoption of programming naming conventions and the increasing emphasis on self-explanatory code, traditional summarizing code comments, which are often similar to code literal meaning, are losing appeal among developers. Instead, developers value supplementary code comments that provide additional information beyond the code itself to facilitate program understanding and maintenance. However, generating such comments typically requires external information resources beyond the code base, and the diversity of supplementary information presents significant challenges to existing methods. This study leverages Issue reports as a crucial external information source and proposes an Issue-based retrieval augmentation method using large language models (LLMs) to generate supplementary code comments. The proposed method classifies the supplementary information found in Issue reports into five categories, retrieves Issue sentences containing this information, and generates corresponding comments using LLMs. In addition, the code relevance and Issue verifiability of the generated comments are evaluated to minimize hallucinations. Experiments conducted on two popular LLMs, ChatGPT and GPT-4o, demonstrate the effectiveness of the proposed method. Compared to existing approaches, the proposed method significantly improves the coverage of manual supplementary comments from 33.6% to 72.2% for ChatGPT and from 35.8% to 88.4% for GPT-4o. Moreover, the generated comments offer developers valuable supplementary information, proving essential for understanding some tricky code.