Capturing spatio-temporal dependencies is the key challenge for applications such as intelligent transportation and energy management. Contrastive learning-based methods have recently emerged as a promising paradigm to address this challenge for their ability to encode discriminant spatio-temporal dependencies. However, most of these methods assign binary labels to contrastive pairs according to their static relationship, which neglect the dynamic dependencies prevalent in data and fundamentally limit their forecasting ability. To address these limitations, we propose a novel Dynamic Soft Contrastive Learning (DynSCon) framework for spatio-temporal forecasting. Specifically, we design a dynamic dependency aware similarity metric through dynamic graph construction and clustering regularization to capture the variation of dependency patterns across time and space dimensions. Contrastive learning is then conducted on node representations across nodes and time supervised by dynamic soft labels derived from this metric to supplement forecasting loss. Thus the learned representation will be able to reveal the underlying dynamic spatio-temporal dependencies and forecasting quality can be enhanced. Extensive experiments on three datasets spanning from traffic flow forecasting to power generation forecasting demonstrates that DynSCon achieves state-of-the-art performance.