中文摘要：

利用中国160个站逐月温度、NCEP再分析和NOAA-CIRES20世纪再分析等资料, 采用统计分析方法, 就反映中国 东部大陆冬季一致性气温变化模态的能力方面, 对多种东亚冬季风指数进行了评估, 探讨了影响东亚冬季风强弱的主要前期因子及其相应的影响过程, 并据此建立了一个预测冬季风指数的预测模型。研究结果表明：1981 年前、后两个阶段, 朱艳峰 2008年定义的东亚冬季风指数都可以很好地反映中国东部大部分地区的冬季气温异常;北美大陆西侧北太平洋中纬度地区 （35°-50°N,145°-130°W）的前期秋季（9-10月）海温、北极喀拉海地区（75°-82°N,65°-85°E）的前秋海冰密集度和东亚中 纬度地区（30°-50°N,80°-140°E）的前秋高空（300-200hPa）温度异常都具有较强的持续性, 异常信号可从前秋一直持续到 冬季, 进而影响东亚冬季风的强度;根据上述3个前期因子建立了东亚冬季风统计预测模型, 评估发现该模型具有较强的预测 能力, 可用于冬季风强度以及相应的中国东部大陆冬季气温的定性预测。

英文摘要：

Using the monthly temperature data from 160 stations of China, the National Centers for Environmental Prediction （NCEP） reanalysis data, the NOAA-Cooperative Institute for Research in Environmental Sciences（CIRES） 20th Reanalysis data, etc,. and the statistical analysis methods, the abilities to reflect the pattern of the consistent variability in winter temperatures over the mainland of eastern China are assessed for a variety of East Asian winter monsoon（EAWM） indices, respectivcly. The precursors affecting the EAWM intensity and associated influencing processes are investigated. According to these precursors, a prediction model is established to predict the FAWM index. The research results show that among these indices, the FAWM index defined by Zhu in 2008 can reflect well anomalous winter temperatures in most parts of eastern China during the two periods before 1981 and after 1981;the previous autumn（September-October） sea surface temperature（SST） in the mid-latitude North Pacific region to the west of the North American continent （35°-50°N, 145°-130° W）, the previous autumn sea ice concentration over the Kara Sea region of the Arctic Uccan（75°-82°N, 65°-85° E）, and the previous autumn high-level （300-200 hPa） air temperature over the mid-latitude region of the East Asia （30°-50°N, 80°-140°E） have a good persistence with anomalous signals persisting from the previous autumn to winter, and therefore may affect the strength of the EAWM; based on these three preceding factors, a statistical prediction model for the EAWM is established. The evaluation results show that the model has a strong predictive ability, and can be used to predict the EAWM intensity and its corresponding winter temperatures over eastern China.