Promoting positive mental health and well-being, especially in adolescents, is a critical yet underexplored area in natural language processing (NLP). Most existing NLP research focuses on clinical therapy or psychological counseling for the general population, which does not adequately address the preventative and growth-oriented needs of adolescents. In this paper, we introduce DeepWell-Adol, a domain-specific Chinese dialogue corpus grounded in positive psychology and coaching, designed to foster adolescents' positive mental health and well-being. To balance the trade-offs between data quality, quantity, and scenario diversity, the corpus comprises two main components: human expert-written seed data (ensuring professional quality) and its mirrored expansion (automatically generated using a two-stage scenario-based augmentation framework). This approach enables large-scale data creation while maintaining domain relevance and reliability. Comprehensive evaluations demonstrate that the corpus meets general standards for psychological dialogue and emotional support, while also showing superior performance across multiple models in promoting positive psychological processes, character strengths, interpersonal relationships, and healthy behaviors. Moreover, the framework proposed for building and evaluating DeepWell-Adol offers a flexible and scalable method for developing domain-specific datasets. It significantly enhances automation and reduces development costs without compromising professional standards—an essential consideration in sensitive areas like adolescent and elderly mental health.