Abstract: In this paper, the daily moth catches data of Cnaphalocrocis medialis（Guenee）in the paddy field at the four representative plant protection stations of southern China during the period from 1994 to 2014 was collected. The four representative stations are Quanzhou in the Guangxi Zhuang Autonomous Region, Xiushan in Chongqing City, Xiangyin in Hunan Province and Zhangjiagang in Jiangsu Province and they represented the rice-growing region of the south China, the rice-growing region of the southwestern China, the rice-growing region between the Nanling Mountains and the Yantze River Valley and the rice-growing region between the Yantze River Valley and the Huaihe River Valley in China respectively. The most significant atmospheric circulation factors related to the C. Medinalis’ moth catches of the four stations in the early or same periods were screened out. Based on the above works, a method of singular cross-spectrum analysis (SCSA) was used to the analysis of the coupling cycles between the moth catches of C. Medinalis of the four stations and the selected circulation factors and the time-varying characteristics of the component sequences was discussed by the reconstruction of coupled component sequence(RCCS). Consequently, the singular cross-spectrum analysis combined with the autoregressive function was used to the extrapolating prediction of C. Medinalis’ occurrence amount. The results were showed that the predicted results were in good agreement with the actual occurred situations. The mean correlation error (ME), average absolute error (MAE), root mean square error (RMSE) and the rate of sign correlation between the predicted values and the actual moth catches amount were found that the ME was -0.071, MAE was 0.349, RMSE was 0.446 and RS was 0.75. They indicated that the prediction error was small, the prediction sequence was stable and the prediction accuracy was high. This method can be applied to the prediction of the occurrence trend of C. Medinalis in southern China.

Key words: Atmospheric circulation factor, Coupled oscillations, Reconstructed coupled component sequence, Autoregressive, Prediction of insect occurrence