Abstract: Exploring the meteorological monitoring and warning model of Spodoptera frugiperda is of great significance for advocating green prevention and control idea of crop diseases and pests, and achieving the goals of early warning, control of first-category crop pest and safe agricultural production. We analyzed the relationships between the occurrence degrees of S. frugiperda and the meteorological factors based on S. frugiperda monitoring data and meteorological data in each province in China during 2019-2020. The meteorological forecasting models of occurrence potential of S. frugiperda in different regions were established by using correlation method, regression analysis, and weighted contingency table analysis method. The objective of this study was to provide scientific basis for pest forecasting and food production safety. The results showed that air temperature was the main meteorological factor affecting the occurrence and development of S. frugiperda, and that high temperature was beneficial for its egg hatching, larval feeding, and pupa surviving. Excessively high temperature inhibited its occurrence and development. The key meteorological factors affecting the occurrence and development of S. frugiperda in different regions were average air temperature in previous winter, March, April, May or the early May, June and average minimum air temperature in the early May, and days of daily maximum air temperature more than 35 ℃ in July. There was a significantly negative correlation between the occurrence degree of S. frugiperda  and the days of maximum air temperature more than 35 ℃ in July. The other key meteorological factors were significantly positively correlated with the occurrence degree of S. frugiperda. The meteorological suitability index of S. frugiperda occurrence potential was calculated and the dynamic occurrence potential forecasting models of meteorological grade were established based on the correlation among the occurrence degree of S. frugiperda and key meteorological factors. Weight coefficients of different key meteorological factors were determined by the weighted contingency table analysis method. The hindcast accuracy of forecasting models was more than 95%, and the extrapolated accuracy of forecasting models was higher than 80% in 2021. The models could be used for monitoring and forecasting of meteorological suitability grades for occurrence potential of S. frugiperda in China.

Key words: Spodoptera frugiperda, occurrence potential, meteorological suitability, occurrence grade, forecasting model.