Generating high-fidelity 3D point clouds is challenging, especially in preserving fine details and structural consistency. In this paper, we propose TRADNet, a Temporal and Regional-Aware Diffusion Network that enhances generation via temporal adaptation and spatial supervision. It comprises: a Timestep-Aware Feature Recalibration (TAFR) module to dynamically balance global-local features, a Detail-Aware Attention Fusion (DAAF) module using multi-scale convolution and attention to refine local structure, and a Region-wise Noise Loss supervising sub-region noise to improve local geometry. Experiments on ShapeNetV2 show TRADNet achieves state-of-the-art 1-NN accuracy across Chair, Airplane, and Car categories, the most reliable measure of generative quality. On the Chair class, TRADNet surpasses TIGER by 1.18% CD and 2.10% EMD, validating the effectiveness of integrating temporal adaptivity and regional supervision into diffusion models.