Decadal predictions have gained immense importance over the last decade because of their use in near-term adaption planning. Computationally expensive coupled model intercomparison project phase 5 general circulation models (GCMs) are initialized every 5 years and they generate the decadal hindcasts with moderate skill. Here we test the hypothesis that computationally inexpensive data-driven models, such as multi-variate singular spectrum analysis (MSSA), which takes care of trends and oscillations, performs similar to GCMs. We pick up one of the most complex variables having low predictability, Indian summer monsoon rainfall (ISMR) and its possible causal sea surface temperatures (SST). We find that the MSSA approach performs similar to the GCMs in simulating SSTs beyond their nonlinear limits of predictability, which is ∼12 months. These SSTs are used for decadal predictions of ISMR and show improved skills compared to the GCMs. We conclude that data-driven models are possible alternatives to computationally expensive GCMs for decadal predictions.

由于在近期适应规划中的使用，十年预测在过去十年中变得非常重要。计算成本高昂的耦合模型比对项目第 5 阶段大环流模型 (GCM) 每 5 年初始化一次，它们生成具有中等技能的年代际后报。在这里，我们测试了这样一个假设，即计算成本低廉的数据驱动模型，例如处理趋势和振荡的多变量奇异谱分析 (MSSA)，其性能类似于 GCM。我们选取了最复杂的可预测性较低的变量之一，即印度夏季风降雨量 (ISMR) 及其可能的因果海面温度 (SST)。我们发现 MSSA 方法在模拟 SST 超出其非线性可预测性限制（约 12 个月）方面的表现与 GCM 相似。这些 SST 用于 ISMR 的十年预测，并且与 GCM 相比显示出改进的技能。我们得出的结论是，数据驱动模型可以替代计算成本高昂的 GCM，用于十年预测。