Traditional Retrieval-Augmented Generation (RAG) systems treat documents in isolation, failing to capture hierarchical relationships between concepts in complex scientific corpora. This limitation compromises answer quality in specialized domains such as climatology, where conceptual dependencies frequently traverse multiple articles. We propose a hybrid architecture that integrates vector search with GraphRAG, Leiden community detection, and cross-encoder re-ranking, achieving gains of 160\% in contextual relevance and 177\% in contextual recall compared to classical RAG. These results demonstrate that unifying local and global retrieval significantly outperforms text-span isolation, paving the way for more effective question-answering systems over dispersed scientific literature.

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