中文摘要：

利用全球谱模式T106L19和增长模繁殖法（BGM）建立了月动力延伸集合预报系统，基于气候海表面温度（SST）和预测海表面温度，设计了三组集合预报试验，一组为气候SST作为模式下边界条件的集合预报试验（CSST试验），另一组为预测SST作为模式的下边界条件的集合预报试验（FSST试验），第三组为前两组试验的集合预报结果之和（AvE30试验），对两种海温强迫分别进行了48个月的试验，并对预报结果进行了检验和分析。结果表明：相对于单一的控制预报，不管是CSST试验还是FSST试验，利用BGM方法制作的初值集合预报能显著提高月平均环流的预报技巧，集合预报对PNA区域的预报技巧改进显著，特别是预测SST强迫有正的贡献；同时考虑初值和边值不确定性影响的集合预报试验（AVE30试验），其全球预报技巧不仅高于控制预报，也分别高于FSST试验和CSST试验，这说明要提高月延伸预报技巧，必须同时考虑初值和边值的影响；大气对SST强迫的响应在模式积分10天开始显著，SST对第二旬和第三句的作用直接影响月平均环流的预报效果，而SST对第二旬和第三旬预报的影响不仅与SST本身变化有关，还与初值有关，不同的初值其作用不同；集合预报对我国夏季月平均温度分布具有较强预报能力，采用预报海温强迫的预报结果，总体上优于气候海温强迫的结果。

英文摘要：

A monthly dynamical extended-range ensemble forecast system is established with a global spectral model （T106L19） and an initial ensemble method of breeding of growing modes （BGM）. Forty-eight examples of monthly integrations are performed with this system, and within each example, there are three sets of monthly ensemble forecasting experiments. The first set, named the CSST experiment, is forced by climatic sea surface temperatures （SSTs）, and the second set, named the FSST experiment, is forced by predicted SSTs, while the last set, named the AVE30 experiment, is the mean of CSST and FSST. Then, the model results are verified and analyzed. Verifications show that the initial ensemble forecasts, based on BGM, can improve the monthly prediction skill remarkably when compared to a single control run, and it is especially precise for the Pacific and Northern America （PNA） region and the predicted SSTs have a positive contribution to the forecasts skill, particularly. The AVE30 experiment, that takes impacts of both initialuncertainties and boundary uncertainties into consideration, has a higher prediction skill than both the CSST and FSST experiments and suggests that we should attach more of an importance to roles of both initial conditions （ICs） and boundary conditions （BCs） to improve the monthly extended-range forecast skill. Meanwhile, analyzing the results indicate that atmospheric responses to SST forcing become significant after the tenth model integration day and the influences of SST forcing on the second and third ten-day period have a direct effect on the prediction skill of the monthly mean circulation. The influences of SST forcing on the second and third ten-day period relate to SSTs variation as well as ICs and the influences differ under different ICs. The monthly ensemble prediction system exhibits a strong capability of forecasting the monthly mean air temperature in China during the summer. The results of FSST forced by predicted SSTs prevail against those of CSST forced