Reasonable seasonal prediction skill for the Indian summer monsoon rainfall has been achieved using the Monsoon Mission (MM) Seasonal Forecast model, at a lead time of 3 months. The ensembles in the MM model are generated by utilizing lagged initial conditions. The possibility of enhancing the lead time is explored by using the burst ensemble approach. Comprehensive seasonal hindcast experiments carried out in this study reveal that the two methods exhibit similar skill scores for the major tropical phenomenon which govern ISMR variability. In general, the model forecasts are slightly under-dispersive but satisfactorily represent the spread-error relationship for major tropical oceanic climate modes. The ratio between the spread and RMSE is small for ISMR forecasts. Though the skill scores for the majority of indices are similar, the monsoon teleconnections seem to be quite sensitive to the initialization strategy. It is found that the burst initialization method provides a gain of 1-month lead time compared to lagged initialization strategy employed in previous studies without compromising the prediction skill. The gain of a months’ lead time with the burst ensemble approach is a tempting and useful proposition, which can be crucial for the policy- and decision-makers.

使用季风任务（MM）的季节预报模型，已经实现了印度夏季季风降雨的合理季节预报技巧，前置时间为3个月。MM模型中的合奏是通过利用滞后的初始条件生成的。通过使用突发合奏方法探索了延长交货时间的可能性。在这项研究中进行的综合季节性后播实验表明，两种方法对控制ISMR变异的主要热带现象显示出相似的技能得分。总的来说，模型预报虽然略有分散，但可以令人满意地表示主要热带海洋气候模式的扩散误差关系。对于ISMR预测，点差与RMSE之间的比率很小。尽管大多数指标的技能得分相似，季风远程连接似乎对初始化策略非常敏感。发现与先前研究中采用的滞后初始化策略相比，突发初始化方法可提供1个月的前置时间，而不会影响预测技能。爆破合奏方法可以节省几个月的准备时间，这是一个诱人且有用的主张，这对于政策制定者和决策者而言至关重要。