Abstract: Different leaf types of tea plants are essential to the ecological suitability patterns under the climate-land interaction, according to the “land diagnosis first, climate matching second”. An adaptive strategy is often required to optimize the planting structure under climate change. Previous large-scale zoning can treat the tea as a single species to focus primarily on climatic variables. It is still lacking in distinguishing between large- and medium/small-leaf types or land constraints. In this study, a climate–land suitability framework was proposed to differentiate the tea leaf types at the national scale. A multi-level validation was constructed from provincial statistics to township-scale remote sensing. National-scale datasets included the 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, and 1,007 medium/small-leaf). The MaxEnt model with GIS techniques was also employed to identify the dominant environmental factors and then simulate potential distribution patterns. Suitability probabilities were classified into four levels—unsuitable, marginally suitable, moderately suitable, and highly suitable—using the natural break. Threshold ranges of dominant factors were quantified for each leaf type and suitability level. Model validation was conducted at multiple spatial scales. The suitability proportions were simulated with provincial planting area statistics. Boundary consistency was verified using field sampling points and fine-scale spatial agreement with meter-level satellite remote sensing imagery. The results indicated that eight environmental variables dominated in 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 °C, soil pH, and elevation. Among them, spring frost frequency was the most influential factor, thereby contributing 52%–69% to model performance. The critical role of frost risk was highlighted in the bud sprouting period. The MaxEnt model demonstrated high prediction accuracy and stability, with the mean AUC values of 0.952 for the large-leaf tea and 0.893 for the medium/small-leaf tea after cross-validation. Zoning patterns were highly consistent with the actual provincial tea planting proportions and known production areas. Planting boundaries were accurately distinguished within short spatial distances (50–60 km). The robustness of the model was verified in both macro-scale pattern recognition and micro-scale boundary identification. Significant differences were observed between leaf types. Large-leaf tea required higher thermal accumulation and moisture availability. The optimal conditions were characterized by ≥10 °C accumulated temperature above 6,200 °C·d, annual precipitation between 1,000–2,000 mm, low spring frost frequency (<5%), and soil pH of 4.5–5.5. The northern planting boundary remained stable along the middle and lower reaches of the Yangtze River, thus expanding northward by 40–50 km during 1991–2020, compared with 1961–1990. In contrast, the medium/small-leaf tea exhibited the stronger cold tolerance and broader ecological amplitude, tolerating lower minimum temperatures and higher frost frequency, with its northern boundary shifting northward by 20–40 km. Furthermore, the highly suitable and moderately suitable areas of large-leaf tea increased by 1.42 and 1.59 million ha, respectively, during 1991–2020, while those of medium/small-leaf tea increased by 0.29 and 1.22 million ha. Land factors—particularly soil pH and elevation—imposed the rigid constraints on expansion, resulting in a spatial pattern as “climatically feasible but land-limited”, although climate warming promoted the northward shift of potentially suitable areas. The findings can provide scientific support for the tea variety selection, regional planting planning, boundary risk assessment, and adaptive strategies under ongoing climate change, with emphasis on future expansion of the land resources.