中文摘要：

利用基于英国Lamb（1950年）发展的大气环流分型方法的Jenkinson（1977年）法对东北地区1951-2002年的月平均海平面气压场（MSLP）进行环流分型.由月平均海平面气压场算出6个环流指数,并由此划分出27种环流类型,分析了其中出现频率最高的5种主要环流类型（N,NW,C,CSW,SW）在不同时间尺度下的变化规律及它们与哈尔滨月平均温度的关系,利用逐步回归方法得到了温度距平的拟合曲线.给出了各种环流类型的月平均降水量和与哈尔滨降水密切相关的C,CSW和SW 3种环流类型对应的平均海平面气压合成图.结果表明：哈尔滨冬季以N,NW型为主,夏季以C,CSW和SW型为主.出现N和NW型时气温偏低,降水偏少;而出现C,CSW和SW型时气温较高,降水偏多.用6个环流指数中的地转风V和大尺度平均温度t可以建立其与温度距平之间的一个统计模式,利用此模式,能解释哈尔滨1951-2002年温度变化方差的77.3%.C,CSW和SW 3种环流类型为哈尔滨的主要降水类型,C型与哈尔滨总降水的相关关系很好,并且近20年来哈尔滨主要以C型降水为主.这种研究大尺度大气环流与区域气候变量一温度、降水之间关系的方法是一种统计降尺度（statistical downscaling）方法,可以用于区域气候预测.

英文摘要：

The classification scheme developed by Jenkinson and Collison （1977） based on a typing scheme of Lamb （1950） is applied to obtain circulation types from the mean sea-level pressure of Northeast of China on a monthly basis. Monthly mean sea-level pressure data from 1951 to 2002 is used to derive six circulation indices and to provide a circulation catalogue with 27 circulation types. Five major types （N, NW, C, CSW, SW） which occurred most frequently are analyzed to reveal their relationships with the temperature of Harbin on various time scales. Stepwise multiple regression is used to reconstruct temperature anomaly. The monthly mean rainfall of all types occurred and the composite maps of the three major types（C, CSW, SW） relevant to Harbin＇s precipitation are studied. The results show that the dominant types in winter are N and NW types. C, CSW and SW types occur frequently in summer. N and NW types favour a negative temperature anomaly and correspond to less rainfall, while C, CSW and SW types often induce a positive temperature anomaly and correspond to more rainfall. Moreover, a successful statistical model can be established with only one of the six indices and large-scale mean temperature. Using the model, 77.3 % of the total variance in the temperature anomaly between 1951 and 2002 can be reconstructed. C type has a close relationship with total rainfall and C type precipitation played a major role in determining the total rainfall of Harbin in recent years. This classification scheme is a statistical downscaling model and its relationships with temperature and precipitation can be used to forecast the regional climate.