Abstract: To investigate the climate suitability of premium green tea in Jiangnan Tea Region and the climatic quality characteristics across different spring picking periods, this study systematically analyzes the climate adaptability for cultivating these green teas and the dynamic changes in climatic quality during 4 distinct spring picking stages. The research is conducted using two key datasets: Long-term meteorological observation records from Jiangnan Tea Region spanning 1961 to 2024, and high-resolution real-time grid data from CLDAS V2.0 covering 2015 to 2024. Methodologically, it is integrated fuzzy mathematics, k-means clustering algorithm, climatic quality index (I_tcq) assessment, and Moran’s spatial autocorrelation analysis to ensure comprehensive and reliable results. Findings indicate that the average climate suitability index of the Jiangnan Tea Region from 1961 to 2024 stood at 0.84, displaying a clear trend of initial increase followed by a gradual decrease. Specifically, the index peaks during 1991-2000 period and thereafter maintained fluctuations at a relatively high level. A spatial comparison between two time periods, 1961-1990 and 1991-2020, reveals that the regions most suitable for green tea cultivation has expanded notably toward the east and north.
During the period of 2015-2024, the climatic quality index (I_tcq) of tea in different spring picking stages, in descending order, is pre-rain tea (2.09), pre-Qingming tea (1.85), early spring tea (1.81), and late spring tea (1.74). Moreover, under the regulation of the spring temperature gradient, the high-value areas of the climatic quality index shows significant differences in spatial distribution. During this period, the climatic quality of pre-rain tea and pre-Qingming tea is highly stable, making them suitable for the layout of high-quality tea gardens, while the climatic quality stability of early spring tea is the poorest.
Further analysis using Moran’s index demonstrates that the climatic quality indices of teas across all spring picking periods exhibited significant positive spatial correlation (Moran’s index ranging from 0.921 to 0.958, with P<0.001), which confirms distinct spatial agglomeration characteristics. In local regions, the spatial correlation is primarily manifested as high-value clustering and low-value clustering. The research can provide scientific support for the differentiated cultivation and climate adaptability management of high-quality green tea in Jiangnan Tea Region.