Image deraining is a critical low-level computer vision task essential for robust outdoor surveillance and autonomous driving systems. While deep learning methods have shown success in aligned training settings, they typically experience significant performance degradation when applied to unseen Out-of-Distribution scenarios due to domain discrepancies between synthetic training data and real-world rain dynamics. This paper proposes a cross-scenario deraining adaptation framework that eliminates the need for paired rainy observations in target domains, utilizing only rain-free background images. The method incorporates a Superpixel Generation module that extracts stable structural priors from source domains using Simple Linear Iterative Clustering, enabling effective rain removal across diverse scenarios.