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. This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initial dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and two-meter temperature two weeks in advance for the continental United States. Regression, quantile regression, and tercile classification tasks using linear models, random forests, convolutional neural networks, and stacked models (a multi-model approach based on the prediction of the individual ML models) are considered. Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to address spatial variability. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. This paper further includes an investigation of feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability.

翻译：在次季节时间尺度上生成关键气候变量（如温度和降水）的高质量预报，长期以来一直是业务预报中的空白。本研究探索了将机器学习模型作为次季节预报后处理工具的应用。我们将滞后的数值集合预报（即成员具有不同初始日期的集合）与包括相对湿度、海平面气压和位势高度在内的观测数据，融入多种机器学习方法，以提前两周预测美国大陆的月平均降水和两米温度。我们考虑了基于线性模型、随机森林、卷积神经网络和堆叠模型（基于单个机器学习模型预测的多模型方法）的回归、分位数回归和三分位数分类任务。与以往通常仅使用集合平均的机器学习方法不同，我们利用了集合预报中嵌入的信息来提升预测精度。此外，我们还研究了对于规划和减灾工作至关重要的极端事件预测。通过将集合成员视为一组空间预报，我们探索了处理空间变异性的不同方法。不同方法之间的权衡可以通过模型堆叠来缓解。我们提出的模型优于气候预报和集合平均等标准基线。本文进一步探讨了特征重要性、使用完整集合与仅使用集合平均之间的权衡，以及处理空间变异性的不同模式。