Domain generalization remains a critical challenge for deploying neural networks, particularly in out-of-distribution object detection. The distributional discrepancy between training (e.g., daytime-sunny) and the realistic condition (e.g., night-rainy) inevitably produces imprecise localization and wrong classification. To address these issues, we propose a unified interaction consistency learning (UICL) framework, a novel single-source domain-generalized method designed to learn intra-class domain-invariant representations. Specifically, we put forth a cross-domain interaction mechanism to exchange region proposals between original and augmented pipelines, enriching the diversity of instance-level representations. Building upon this, we propose prediction-guided consistency learning to unify the interaction mechanism and harmonize the cross-domain representations, contributing to a discriminative prediction distribution under domain shift. In addition, we devise a cyclic interaction resilient detection strategy, which mitigates inaccurate predictions suffering from partial occlusion and ambiguous boundaries among different domains. Extensive experiments evidence that UICL significantly improves the robustness of detectors over several target domains, achieving state-of-the-art generalization performance on the diverse weather benchmark. The code is available at https://github.com/zhangpeng2001/uicl.