Semantic segmentation of outdoor 3D point clouds degrades significantly under adverse weather, as rain, fog, and snow corrupt the geometric structure of LiDAR returns through backscatter insertion, range-dependent attenuation, and volumetric scattering. Existing domain generalization methods constrain feature values directly, which becomes less effective when weather-induced perturbations alter the local neighborhood topology that underlies feature aggregation. This work proposes Geometric Diffusion Consistency (GDC), a training-time regularizer that enforces consistent feature propagation behavior across geometrically divergent views of the same point cloud. A dual-view augmentation pipeline generates training pairs through weak and strong perturbations, where the strong branch incorporates dual-mode atmospheric extinction modeling, semantic-aware geometric corruption, and weather-coordinated structural perturbation. A lightweight learnable diffusion operator, implemented via sparse convolutions with a gated residual connection, propagates encoder bottleneck features through local voxel neighborhoods. The consistency loss aligns diffused representations at corresponding points across views, preserving topological relationships essential for dense prediction while allowing feature values to adapt to altered geometry. On the SemanticKITTI to SemanticSTF domain generalization benchmark, GDC achieves 38.6% mIoU, exceeding the previous best method by 3.8%, with consistent improvements across dense fog, light fog, rain, and snow conditions.