Word sense disambiguation (WSD) is typically studied in synchronic settings, where the mapping from contextualized occurrences to sense labels is assumed to be temporally stable. This assumption may not hold for historical texts, in which meanings may emerge, shift, or disappear over time. We study diachronic WSD for Ancient Chinese, a challenging scenario involving long-term semantic evolution and substantial historical variation. We introduce Ancient Chinese Diachronic Word Sense Disambiguation Benchmark (AC-DiaWSD), a large-scale WSD dataset constructed from the Twenty-Four Histories, containing about 24K manually annotated instances across 12 dynasties from the Western Han to the Qing. We further propose DynaSense, a model-agnostic framework that enhances pre-trained language models with contrastive sense learning and dynasty-conditioned representation adaptation. Experiments with multiple PLMs and LLMs show that existing models struggle with diachronic WSD, while DynaSense consistently improves accuracy and F1 across backbones.