To enhance detection accuracy in adverse weather conditions, domain adaptation methods that extract domain-invariant features from both the source and target domains have been present for one-stage detectors. However, the use of pseudo-labels in the instance-level domain adaptation inevitably introduces noise. To tackle this challenge, we propose an auxiliary domain-guided adaptive one-stage detection method. Firstly, a generative network is used to transform source domain images into the auxiliary domain. To form a suitable auxiliary domain that can provide reliable guidance for instance-level adaptation of the detector, the generated images are required to possess a similar style to that of the target domain, while also being restricted to maintaining the same object categories and location information as the source domain images. Secondly, for instance-level adaptation, we treat the same object from different domains as positive samples and different objects as negative samples, and utilize contrastive learning to ensure that only the domain shift, rather than other domain-irrelevant disparities, is reduced during the adaptation process. Experimental results demonstrate that our approach obtains a significant improvement over state-of-the-art (SOTA) algorithms on real datasets captured under adverse weather conditions.