Legal Judgment Prediction (LJP) has emerged as a fundamental aspect of legal AI systems, encompassing both civil and criminal cases. In this paper, we focus on civil LJP, which aims to predict the judgment result based on both case facts and the plaintiff’s claim. Existing approaches rely on post-hoc facts summarized by judges, which has proposed leveraging actual fact (i.e., court debate data) to make judgment prediction. However, this approach still suffers from limitations (e.g., data noise), resulting in suboptimal predictions. To this end, we propose a post-hoc facts augmented LJP (PF-LJP) to further explore this “real pattern”. Specifically, we encourage the court debate data to be closer to its corresponding post-hoc fact in the vector space with the contrastive learning and distribution alignment method. During the inference, only the court debate data is employed to yield results without any post-hoc fact. Experimental results on a real-world dataset validate the effectiveness of PF-LJP.