[Aim]  To develop an accurate prediction method suitable for local conditions and thereby improve the prevention and control of the tea green leafhopper (Empoasca onukii), a major pest of tea in all tea-growing regions of China. [Methods]  We conducted long-term investigations of leafhopper density in representative tea gardens in four major regions. Based on climate data and the population dynamics of the species (2011 to 2017), we used a fuzzy comprehensive evaluation method to develop a prediction method for the first population peak of leafhoppers in each region. A Web-based monitoring and early warning platform for tea plant diseases and insect pests was also established. [Results]  This study identified the occurrence patterns of tea green leafhoppers in four representative tea-growing areas from 2011 to 2017. This species generally had either single, or double, peaks of abundance. The first peak was usually between May and July, which was the most severe period of crop damage in a year. By identifying key factors influencing the overwintering population, and analyzing meteorological data (specifically, the average February temperature and the sum of the lowest temperatures from December to February), we developed both a prediction method and an early warning platform for the first peak of abundance of this pest. Testing this method with actual leafhopper abundance data from 2018 to 2022 indicates that it has an overall accuracy rate of 83.64%. [Conclusion]  The new early warning method has high accuracy and the early warning platform provides various services, including data analysis, data transmission, query sharing, and early warning of peak abundance. Tea management departments and agricultural technicians can promote the use of this method for predicting and forecasting peak abundance of the tea green leafhopper.