Retrieval shapes how language models access and cite knowledge in retrieval-augmented generation (RAG). In historical research, the goal is often to locate the exact record for a specific regnal month, where temporal alignment matters as much as topical relevance. This is especially challenging for Classical Chinese annals: time is encoded in terse, implicit, non-Gregorian reign phrases that are context-dependent, so semantically plausible evidence can still be temporally invalid. We introduce **ChunQiuTR**, a time-keyed retrieval benchmark built from the **Spring and Autumn Annals** and its exegetical tradition. It organizes records by month-level reign keys and includes chrono-near confounders that mimic real retrieval failures. We propose **CTD** (Calendrical Temporal Dual-encoder), a time-aware dual-encoder combining Fourier-based absolute context with relative offset biasing. Experiments show consistent gains over semantic dual-encoder baselines under time-keyed evaluation. We will release ChunQiuTR and code after the anonymity period.