In Model-Based Reinforcement Learning (MBRL), an agent learns to make decisions by building a world model that predicts the environment's dynamics. The accuracy of this world model is crucial for generalizability and sample efficiency. Many works rely on pixel-level reconstruction, which may focus on irrelevant, exogenous features over minor, but key information. In this work, to encourage the world model to focus on important task related information, we propose an augmentation to the world model training using a temporal prediction loss in the embedding space as an auxiliary loss. Building our method on the DreamerV3 architecture, we improve sample efficiency and stability by learning better representation for world model and policy training. We evaluate our method on the Atari100k and Distracting Control Suite benchmarks, demonstrating significant improvements in world model quality and overall MBRL performance.