In contrastive learning for time series, contrasting similar time series instances or values from adjacent timestamps within a time series leads to ignore their inherent correlations, deteriorating the quality of learned representations. To address this issue, we propose SoftCLT, a simple yet effective soft contrastive learning strategy for time series. This is achieved by introducing instance-wise and temporal contrastive loss with soft assignments. Specifically, we define soft assignments for 1) instance-wise contrastive loss by the distance between time series on the data space, and 2) temporal contrastive loss by the difference of timestamps. SoftCLT is a plug-and-play method for time series contrastive learning that improves the quality of learned representations. In experiments, we demonstrate that SoftCLT consistently improves the performance in various downstream tasks.