Modern applications provided personalized recommendations across diverse scenarios, including the homepage, local pages, and live streams on platforms like TikTok. These scenarios exhibit varying user behavior patterns, resulting in heterogeneous yet interrelated distributions. Existing Multi-Scenario Recommendation (MSR) methods usually use parameter-sharing networks for shared features and scenario-specific networks for unique features. However, these methods fail to handle different distribution across scenarios, resulting in representation entanglement and localization, which hinder effective knowledge transfer and compromise performance. In this paper, we propose a Distribution-aware Re-representations (DAR) method for MSR. Its core idea is to construct distribution-aware prototype spaces and learn disentangled re-representations around global prototypes. Specifically, DAR employs a Multi-gate Mixture of Experts (MMoE) to obtain scenario-shared representations, and uses independent networks to learn scenario-specific representations. These representations are then projected into scenario-shared and scenario-specific prototype spaces, producing scenario-shared re-representations (capturing global information) and scenario-specific re-representations (focusing on distributional differences). During this process, DAR utilizes Unbalanced Optimal Transport (UOT) to compute the transport relationships between representations and global prototypes, taking these as pseudo-labels for re-representation learning. Moreover, to prevent prototype entanglement, a matrix orthogonalization constraint ensures independence among global prototypes. The effectiveness of DAR is demonstrated through extensive offline experiments conducted on four datasets, as well as online A/B tests on a video platform.