The wide prevalence of staining variations in digital pathology presents a significant obstacle, often undermining the effectiveness of diagnosis and analysis. The current strategies to counteract this issue primarily revolve around Stain Normalization (SN) and Stain Augmentation (SA). Nonetheless, these methodologies come with inherent limitations. They struggle to adapt to the vast array of staining styles, tend to presuppose linear associations between color spaces, and often lead to unrealistic color transformations. In response to these challenges, we introduce RandStainNA++, a novel method seamlessly integrating SN and SA. This method exploits the versatility of random SN and SA within randomly selected color spaces, effectively managing variations for the foreground and background independently. By refining the transformations of staining styles for the foreground and background within a realistic scope, this strategy promotes the generation of more practical staining transformations during the training phase. Further enhancing our approach, we propose a unique self-distillation method. This technique incorporates prior knowledge of stain variation, substantially augmenting the generalization capability of the network. The striking results yield that, compared to conventional classification models, our method boosts performance by a significant margin of 16-25%. Furthermore, when juxtaposed with baseline segmentation models, the Dice score registers an increase of 0.06. The codes are available at https://github.com/wagnchogn/RandStainNA-plusplus.