In the domain of multi-agent reinforcement learning (MARL), the limited information availability, complex agent interactions, and individual capabilities among agents often pose a bottleneck for effective decision-making. Previous studies frequently fall short due to insufficient consideration of these multi-dimensional challenges. Thus, this paper introduces a novel methodology, termed Multi-scale Decision Enhancement (MaDE), anchored by a dual-wise bisimulation framework for pre-training agent encoders. The MaDE framework aims to facilitate decision-making across three pivotal dimensions: macroscale awareness, mesoscale coordination, and microscale insight. At the macro level, a pretrained global encoder captures a situational awareness map to guide overall strategies. At the meso level, specialized local encoders generate cluster-based representations to promote inter-agent cooperation. At the micro level, individual agents focus on the accurate decision-making process. Empirical evaluations validate that MaDE outperforms state-of-the-art methods in various multi-agent environments, which shows the potential to tackle the intricate challenges of MARL, enabling agents to make more informed, coordinated, and adaptive decisions. Code is available at https://github.com/paper2023/MaDE.