Reconstruction-based Model-Based Reinforcement Learning (MBRL) agents, such as Dreamer, often fail to discard task-irrelevant visual distractions that are prevalent in natural scenes. In this paper, we propose a reconstruction-free MBRL agent, called DreamerPro, that can enhance robustness to distractions. Motivated by the recent success of prototypical representations, a non-contrastive self-supervised learning approach in computer vision, DreamerPro combines Dreamer with prototypes. In order for the prototypes to benefit temporal dynamics learning in MBRL, we propose to additionally learn the prototypes from the recurrent states of the world model, thereby distilling temporal structures from past observations and actions into the prototypes. Experiments on the DeepMind Control suite show that DreamerPro achieves better overall performance than state-of-the-art contrastive MBRL agents when there are complex background distractions, and maintains similar performance as Dreamer in standard tasks where contrastive MBRL agents can perform much worse.