Abstract: Clarifying the ecological suitability patterns of different leaf types of tea plants under the combined influences of climate and land factors is essential for optimizing planting structure and improving adaptive strategies under climate change. Previous large-scale zoning studies generally treated tea as a single species and focused primarily on climatic variables, without distinguishing between large-leaf and medium/small-leaf types or incorporating comprehensive land constraints. To address these limitations, this study integrated national-scale datasets including daily meteorological observations from 2,387 stations (1961–2020), soil type, soil pH (30–100 cm depth), elevation, and 1,097 georeferenced tea garden occurrence records (205 large-leaf, 1,007 medium/small-leaf). The MaxEnt model combined with GIS techniques was employed to identify dominant environmental factors and simulate potential distribution patterns. Suitability probabilities were classified into four levels—unsuitable, marginally suitable, moderately suitable, and highly suitable—using the natural break method. Threshold ranges of dominant factors were quantified for each leaf type and suitability level. Model validation was conducted at multiple spatial scales by comparing simulated suitability proportions with provincial planting area statistics, verifying boundary consistency using field sampling points, and examining fine-scale spatial agreement with meter-level satellite remote sensing imagery. The results indicated that eight environmental variables jointly controlled the distribution of both leaf types: spring frost frequency, annual precipitation, relative moisture index from June to August, average temperature of the coldest month, average relative humidity from March to September, accumulated temperature ≥10 ℃, soil pH, and elevation. Among these, spring frost frequency was the most influential factor, contributing approximately 52%–69% to model performance, highlighting the critical role of frost risk during the bud sprouting period. The MaxEnt model demonstrated high predictive accuracy and stability, with mean AUC values of 0.952 for large-leaf tea and 0.893 for medium/small-leaf tea across repeated cross-validations. Zoning results were highly consistent with actual provincial tea planting proportions and known core production areas, and accurately distinguished planting boundaries within short spatial distances (50–60 km), confirming the robustness of the model in both macro-scale pattern recognition and micro-scale boundary identification.Significant differences were observed between leaf types. Large-leaf tea requires higher thermal accumulation and moisture availability, with optimal conditions characterized by ≥10 ℃ accumulated temperature above 6,200 ℃·d, annual precipitation between 1,000–2,000 mm, low spring frost frequency (<5%), and soil pH of 4.5–5.5. Its northern planting boundary remains generally stable along the middle and lower reaches of the Yangtze River, expanding northward by approximately 40–50 km during 1991–2020 compared with 1961–1990. In contrast, medium/small-leaf tea exhibits stronger cold tolerance and broader ecological amplitude, tolerating lower minimum temperatures and higher frost frequency, with its northern boundary shifting northward by approximately 20–40 km. During 1991–2020, the highly suitable and moderately suitable areas of large-leaf tea increased by 1.42 and 1.59 million ha, respectively, while those of medium/small-leaf tea increased by 0.29 and 1.22 million ha. Although climate warming has promoted the northward shift of potential suitable areas, land factors—particularly soil pH and elevation—impose rigid constraints on expansion, resulting in a spatial pattern characterized as “climatically feasible but land-limited.” This study establishes a climate–land integrated suitability framework that differentiates tea leaf types at the national scale and constructs a multi-level validation system from provincial statistics to township-scale remote sensing. The findings provide scientific support for tea variety selection, regional planting planning, boundary risk assessment, and adaptive agricultural policy formulation under ongoing climate change, and emphasize that future expansion strategies should follow the principle of “land diagnosis first, climate matching second.”