Mainstream large language models adopt an autoregressive generation paradigm, requiring text to be generated token by token, which makes it difficult to meet the demands of efficient inference in practical applications. Diffusion language models achieve parallel decoding through iterative denoising and therefore offer certain theoretical advantages in inference efficiency, making them an important technical direction for improving the inference efficiency of large language models. However, the generation quality of existing diffusion language models still leaves considerable room for improvement. Contrastive decoding is a widely proven effective method for enhancing the generation quality of language models, whose core idea is to calibrate a stronger expert distribution using a weaker amateur distribution. However, existing contrastive decoding methods are primarily designed for autoregressive language models and typically require an additional independent model to provide the amateur distribution, which significantly increases inference overhead and undermines the core theoretical efficiency advantage of diffusion language models. To address this issue, this paper proposes DiffuCD, a contrastive decoding approach designed specifically for diffusion language models. The approach is based on a key observation: during the denoising process of diffusion language models, predictions at earlier timesteps are of lower quality due to limited contextual information, while prediction quality progressively improves as denoising proceeds. This property naturally satisfies the requirement of contrastive decoding for distributions of varying quality. Based on this observation, DiffuCD directly uses the predicted distributions from historical timesteps as amateur distributions and treats the predicted distribution at the current timestep as the expert distribution, thereby calibrating the current distribution. Extensive experiments are conducted on 4 diffusion language models and 6 benchmarks. Experimental results demonstrate that DiffuCD can consistently improve generation quality across various models and tasks, while maintaining nearly identical throughput and memory usage compared to the baseline without contrastive decoding, effectively preserving the theoretical inference efficiency advantage of diffusion language models.