Continuous Sign Language Recognition aims to predict gloss sequences from unsegmented sign language videos. Existing methods typically rely on Connectionist Temporal Classification (CTC) loss for implicit alignment, which is the most dominant loss function for current sequence alignment tasks but causes low-entropy alignment and weak visual-semantic correspondence learning. To address this issue, we propose an explicit temporal-semantic supervision framework that provides dense signals beyond CTC. Specifically, we design the Gloss-Temporal Module (GTM) to construct sustained gloss-to-frame interval supervision by using an LLM to integrate three types of information from RGB videos. We further introduce a joint optimization method with frame-level and sequence-level objectives, coupled with a curriculum learning strategy, enabling a transition from local to global alignment and improving the model’s learning of visual-semantic features. The results show that dense temporal supervision substantially alleviates alignment collapse and enables even simple RGB-only baseline models to achieve competitive performance among state-of-the-art methods without extra cues, while the recognizer converges significantly faster.