Domain generalizable person re-identification (DG-ReID) aims to train models on multi-source domains for robust generalization to unseen target domains. However, prevailing methods often overemphasize inter-domain style mixing, thereby neglecting inherent style shifts from intra-domain camera viewpoint variations. Furthermore, their inter-domain fusion strategies frequently rely on overly stochastic single-instance style transfers, which inadequately capture holistic domain styles and limit effective exploration of novel style combinations. To address these shortcomings, we propose a Hierarchical Style Mixing (HSM) framework. HSM distinctively augments style diversity via two complementary modules: Camera-Aware Mixing (CAM) and Domain Fusion Mixing (DFM). CAM utilizes dynamic, camera-specific style queues for targeted intra-domain, cross-camera style fusion, mitigating viewpoint-related biases. DFM, for each sample, innovatively sources a batch of instances from a different domain, analyzes their collective style statistics to define a foundational style-space, and then probabilistically explores beyond this defined space to synthesize novel, more exploratory style data for robust fusion. The synergistically fused features from CAM and DFM significantly enhance the model’s discriminative power. Extensive experiments on standard benchmarks demonstrate that our method outperforms state-of-the-art DG-ReID approaches under multi-source settings.