Producing high-quality forecasts of key climate variables such as temperature and precipitation on subseasonal time scales has long been a gap in operational forecasting. Recent studies have shown promising results using machine learning (ML) models to advance subseasonal forecasting (SSF), but several open questions remain. First, several past approaches use the average of an ensemble of physics-based forecasts as an input feature of these models. However, ensemble forecasts contain information that can aid prediction beyond only the ensemble mean. Second, past methods have focused on average performance, whereas forecasts of extreme events are far more important for planning and mitigation purposes. Third, climate forecasts correspond to a spatially-varying collection of forecasts, and different methods account for spatial variability in the response differently. Trade-offs between different approaches may be mitigated with model stacking. This paper describes the application of a variety of ML methods used to predict monthly average precipitation and two meter temperature using physics-based predictions (ensemble forecasts) and observational data such as relative humidity, pressure at sea level, or geopotential height, two weeks in advance for the whole continental United States. Regression, quantile regression, and tercile classification tasks using linear models, random forests, convolutional neural networks, and stacked models are considered. The proposed models outperform common baselines such as historical averages (or quantiles) and ensemble averages (or quantiles). This paper further includes an investigation of feature importance, trade-offs between using the full ensemble or only the ensemble average, and different modes of accounting for spatial variability.

翻译：生成次季节时间尺度上气温和降水等关键气候变量的高质量预报长期以来一直是业务预报的空白。近期研究表明，使用机器学习模型推进次季节预报已取得初步成效，但仍存在若干未解决问题。首先，以往的多种方法将基于物理的集合预报均值作为模型输入特征，但集合预报本身包含超越集合均值的有助于预测的信息。其次，现有方法侧重于平均性能，而极端事件的预报对规划和减灾具有更重要意义。第三，气候预报对应空间变化的预报集合，不同方法对响应空间变异性的处理方式各异。模型堆叠可能缓解不同方法间的权衡问题。本文描述了应用多种机器学习方法，利用基于物理的集合预报和相对湿度、海平面气压、位势高度等观测数据，提前两周预测美国本土月平均降水量和两米气温的研究。研究涵盖基于线性模型、随机森林、卷积神经网络和堆叠模型的回归、分位数回归及三分位分类任务。所提模型优于历史平均值（或分位数）和集合平均值（或分位数）等常见基线。本文进一步探究了特征重要性、完整集合与仅使用集合均值的权衡，以及处理空间变异性的不同模式。