Multiphase contrast-enhanced computed tomography (CT) is clinically significant in providing vascular structure and lesion phase-specific enhancements. Yet, its clinical utility is constrained by intrinsic contrast agent-associated risks (e.g., nephrotoxicity, allergic reactions) and multiphase cumulative radiation exposure. To tackle this, synthesizing contrast-enhanced CT (CECT) using non-contrast CT (NCCT) offers a potential alternative. However, achieving a high-quality synthesis of multiphase CECT remains challenging due to the contrast agent (CA)-induced complex contrast flow dynamics and the specific variations across phases. Therefore, this paper proposes a contrast flow pattern and cross-phase specificity-aware diffusion model for NCCT-to-multiphase CECT synthesis. Specifically, a contrast flow pattern learning mechanism is integrated into the conditional diffusion model, which enables orderly phase transitions while ensuring anatomically and temporally coherent enhancement synthesis. Furthermore, a phase distinction network is introduced to align cross-phase specificity features with the contrast features in synthesized CECT images. Experimental results on multicenter abdomen CT datasets have demonstrated the superiority of our method compared to state-of-the-art methods.