The importance of deciduous plants’ autumn phenological phase is reflected not only in the material cycle but also in the fall foliage ecotourism industry. However, the lack of observation data has made it difficult to model the phase of deciduous plants in the high-elevation karst region. Based on the data between 2001 and 2020, methods including multiple linear regression, multivariate binomial regression, robust regression, ridge regression, elastic net, and generalized additive were utilized to find out the correlation of the temperature and the precipitation with Cotinus coggygria’s leaf coloration peak date variation in the related regions of China. The mean absolute error, root mean squared error, coefficient of determination, and ratio of performance to interquartile distance were applied to evaluate the prediction accuracy. Generalized additive model's better performance suggested its capacity as a useful tool in prediction. The results showed that the increase of prophase temperature and the decrease of prophase precipitation could delay the leaf coloration peak date, while the date could be earlier on the contrary. Besides, the number of days with the daily maximum temperature continuously being above 20°C before November 10th exerted the highest contribution in the prediction of the date. Moreover, we showed that a higher average diurnal temperature range in mid-May could defer the date as an objective contributor in prediction. This indirectly reveals the importance of spring phenological phase in predicting the autumn phenological phase. Finally, the model forecasted a trend of the leaf coloration peak date delay from 2021 to 2100 under different emissions scenarios, showing the effects of warming on the plant's growth season and even the development of ecotourism.