【目的】 以湖南省郴州市烟田害虫斜纹夜蛾Spodoptera litura (Fabricius)历年虫情资料为例，探讨利用异地和前一年气象因子构建其当年害虫发生动态预测模型的可行性。【方法】 收集整理2000-2013年长沙、2000-2015年郴州、广州、南昌4地历史气象资料（如：温度、湿度、降雨量、日照时数等）以及2000-2015年郴州地区烟田斜纹夜蛾的历史虫情（如：成虫和幼虫的年发生量等）资料，利用SPSS软件中逐步回归分析法和卡平方分析法构建和筛选有效的多因子预测模型。【结果】 利用郴州市16年斜纹夜蛾虫情资料，郴州、长沙2个不同地区对应年份的共170个气象因子，广州、南昌2个不同地区对应年份的共104个气象因子，按回归模型（方程）满足①总体或某因子显著水准P≤0.05；②多重共线性方差膨胀系数最大值VIF≤5；③理论模拟值或预测值与实测值的χ²适合性检验值（Karl. Pearson）P_（χ²_）≥0.05的3个基本条件，筛选模型中的影响因子和有效预测模型，共获得显著影响因子67个，建立显著有效模型16个。卡方检验结果表明，16个模型的全部回测结果均满足χ²<χ²_0.995，即所建这些模型的回代预测值与实测值几乎完全相同；对未参与建模的年份的幼虫成虫进行预测，共获得4组卡方累计值，第1组满足χ²<χ²_0.995，第2组满足χ²<χ²_0.950，第4组满足χ²<χ²_0.500，第3组满足χ²<χ²_0.250。【结论】 合理利用异地和前一年气象因子预测同一地点当年害虫年发生动态的方法是准确可行的。该方法与以往仅利用当年和本地气象因子进行预测的方法相比，具有明显的下列优点：①可提前发布长期预报的时间；②某地只要有足够时长的系统的历史虫情资料积累，即便没有当地对应的历史气象资料，同样可以开展预测模型的构建。而以上两点正是人们一直在寻求要解决的现实问题，因此本研究结果具有较强的可实用性和可推广性。

 [Objectives]  To determine the feasibility of using meteorological factors from different sites to develop models to predict the annual population dynamics of insect pests. [Methods]  Meteorological data, including temperature, humidity, rainfall and sunshine duration in Changsha from 2000 until 2013, and from Chenzhou, Guangzhou and Nanchang from 2000 until 2015, and data on the abundance of Spodoptera litura (Fabricius) in Chenzhou from 2000 until 2015, were analyzed using stepwise regression and Chi-square tests to develop effective, multivariate forecasting models. [Results]  Explanatory variables were selected based on a 16-year-long S. litura dataset from Chenzhou city, 170 meteorological factors for the corresponding years in Chenzhou and Changsha, and 104 meteorological factors for corresponding years in Guangzhou and Nanchang. For regression models and Chi-square tests, alpha was ≤ 0.05 and variance inflation factors (VIF) ≤ 5. Seventy-four significant explanatory variables were obtained and 16 effective models were created. The results of Chi-square tests indicate that χ² for back-substitution results from all 16 models was between χ²<χ²_0.995, in other words, there was no significant difference between the back-substituted predicted values and the observed values. We obtained four groups of Chi-square cumulative values; the first group was within χ²<χ²_0.995, the second group within χ²<χ²_0.950, the fourth group within χ²<χ²_0.500, and the third group within χ²<χ²_0.250. [Conclusion]  It is accurate and feasible to predict the annual occurrence dynamics of pests on the basis of meteorological factors from different locations obtained in previous years. Compared with predictive methods using only data from the current year and local meteorological factors, the main advantages of this approach are that, provided there is sufficient historical information on pest abundance and meteorological data, it can provide predictions a long time in advance and in the absence of local historical meteorological data. Since there is often insufficient local data to create accurate models the results of this study have broad general applicability.