Synthetic data-driven methods perform well on image rain removal task, but they still face many challenges in real rainfall scenarios due to the complexity and diversity of rainy patterns. In this paper, we propose a new generic paradigm for real image deraining from the perspective of synthesizing data covering more rainy patterns and constructing image rain removal networks with strong generalization performance. Firstly, instead of simply superimposing rain layers, we integrate various rainy patterns and design a phenomenal pipeline that incorporates multiple degradation types. Secondly, we construct a Patterns-aware Rain Removal Network (PRRN), which learns from both synthetic and real data simultaneously. In addition, to eliminate the inevitable distribution differences between synthetic and real data, we design a new Multi-representation Inter-domain Alignment Module (MIAM) in PRRN. By using multiple parallel submodules, MIAM achieves alignment of data domains in multiple feature subspaces. Based on several authoritative objective evaluation metrics, we successfully validate the effectiveness and robustness of the proposed method in real scenarios through extensive experiments carried out on five challenging real datasets.