<p>The rapid advancement of urbanization has intensified scrutiny on the sustainable development of historical towns. A fundamental principle for their revitalization is the enhancement of local residents’ living environments while rigorously preserving their heritage value. This study takes Pingle Ancient Town in Sichuan Province, China, as its empirical focus. It classifies the town’s landscape morphology based on objectives of climatic comfort and proposes a systematic methodology for its identification and optimization. A critical precursor to effective climate-responsive design is the isolation of key landscape factors—those elements whose strategic modification can significantly improve thermal comfort during summer conditions. This study introduces a novel framework that integrates a landscape morphology classification system with empirically validated key landscape factors—Large Surface Water Body (LSWB), Greenery Coverage Ratio (GCR), and Green Space Enclosure Degree (GED)—to establish a replicable, simulation-based optimization process. Key quantified outcomes include a context-specific 40% GCR threshold for community environments (derived from Pingle’s hot-humid low-wind conditions, requiring local calibration for other sites) and the identification of “Green Space Adjacent to Water” as the optimal riparian configuration. The methodology proceeds through three sequential stages: (1) identification of defining morphological characteristics, (2) establishment of discrete morphological typologies, and (3) determination and validation of optimized morphological schemes. This structured approach yields concrete, actionable guidelines for implementing climate-conscious landscape design, thereby significantly augmenting the practical applicability and translational potential of the research.</p>