Recently, advancements in AI counseling based on large language models have shown significant progress. However, existing studies employ a one-time generation approach to synthesize multi-turn dialogue samples, resulting in low therapy fidelity and a lack of deeper understanding of the therapeutic logic underlying each response turn. In this work, we propose a novel data synthesis framework, CATCH, designed to address the challenges above. Specifically, to improve therapy fidelity, we introduce the Progressive Dialogue Synthesis strategy to systematically derive key elements from the client's self-report and organize them into structured outlines, thereby generating a counseling dialogue dataset aligned with therapeutic intervention principles. To enhance therapy logic, we propose the Memory-Driven Dynamic Planning thinking pattern, which clarifies decision-making motivations for each dialogue turn. This pattern incorporates memory enhancement, global planning, and strategy reasoning, enabling the development of a collaborative multi-agent iterative optimization method that synthesizes complete chains of thought in counseling dialogues. Extensive experiments demonstrate that CATCH significantly enhances the fidelity and logical coherence in AI counseling.