Legal QA systems may benefit from training data that is expert-verified and associated with statutory provisions, as fluent generation alone cannot guarantee legally relevant and citation-supported outputs. However, existing Korean legal datasets provide limited support for legal QA and statute-associated response generation. To address this gap, we introduce KoLegalQA, a large-scale Korean legal question–answer corpus designed for research on legal QA and explanation-oriented legal response generation in real-world consultation scenarios. The dataset comprises 19k consultations collected from government-operated services, with all responses originally authored or verified by licensed legal professionals. Unlike prior resources, KoLegalQA provides explicit statutory references and clause-level summaries, enabling research on citation-associated and explanation-oriented legal response generation. We benchmark six Korean-capable LLMs using both automated evaluation (G-Eval) and human assessment across multiple criteria, including legal correctness, reasoning quality, and citation relevance. Experimental results show that fine-tuning on KoLegalQA generally improves legal reasoning validity and statute-associated response generation across most evaluated models. We present this resource as a practical benchmark dataset for Korean legal NLP research. Dataset splits, preprocessing scripts, and evaluation code will be publicly released to support reproducible research.