Most traditional fully supervised person search approaches are only applicable to one data domain and have limited generalization ability on unknown data domains. Researchers have recently started studying domain-adaptive person search, aiming to improve the generalization ability of the involved model for unknown target domains, where domain alignment and reliable positive and negative generations are the primary challenges. To this end, herein, a domain-adaptive person search approach with diverse images and instance augmentation is proposed, which aims to effectively achieve domain alignment and reliable positive and negative generations. This approach introduces two novel modules: source-domain image augmentation and negative-enhanced re-id learning modules. The former aims to improve the domain adaption ability of the involved model and the detection precision on target domains by only enhancing source-domain data diversity. Meanwhile, the latter introduces a diverse-negative mining module to enrich the diversity of negatives and improve the discriminability of learned re-id features. The proposed modules were only used during training, which did not increase the involved test inference time. Experiments were performed on two widely employed person search datasets: CUHK-SYSU and PRW, demonstrating the effectiveness of the proposed approach and its superiority over traditional people search approaches. For instance, the proposed approach achieves mean average precision (mAP) of 40.8% on the PRW test set, indicating higher performance than that of the existing domain-adaptive approach DAPS by 6.1 percentage points.