Climate change detection and attribution (D&A) is concerned with determining the extent to which anthropogenic activities have influenced specific aspects of the global climate system. D&A fits within the broader field of causal inference, the collection of statistical methods that identify cause and effect relationships. There are a wide variety of methods for making attribution statements, each of which require different types of input data and each of which are conditional to varying extents. Some methods are based on Pearl causality (experimental interference) while others leverage Granger (predictive) causality, and the causal framing provides important context for how the resulting attribution conclusion should be interpreted. However, while Granger-causal attribution analyses have become more common, there is no clear statement of their strengths and weaknesses and no clear consensus on where and when Granger-causal perspectives are appropriate. In this prospective paper, we provide a formal definition for Granger-based approaches to trend and event attribution and a clear comparison with more traditional methods for assessing the human influence on extreme weather and climate events. Broadly speaking, Granger-causal attribution statements can be constructed quickly from observations and do not require computationally-intesive dynamical experiments. These analyses also enable rapid attribution, which is useful in the aftermath of a severe weather event, and provide multiple lines of evidence for anthropogenic climate change when paired with Pearl-causal attribution. Confidence in attribution statements is increased when different methodologies arrive at similar conclusions. Moving forward, we encourage the D&A community to embrace hybrid approaches to climate change attribution that leverage the strengths of both Granger and Pearl causality.

翻译：气候变化检测与归因研究旨在确定人类活动对全球气候系统特定方面的影响程度。该领域隶属于更广泛的因果推断范畴，即通过统计方法识别因果关系的学科体系。目前存在多种归因方法，各自需要不同类型的输入数据，且其适用条件存在差异。部分方法基于珀尔因果框架（实验干预），而另一些则运用格兰杰（预测性）因果理论，不同的因果框架为归因结论的解读提供了重要语境。尽管基于格兰杰因果的归因分析日益普及，但对其优劣尚无明确界定，也未就其在何种情境下适用形成共识。本前瞻性论文为基于格兰杰因果的趋势与事件归因方法提供了形式化定义，并与评估人类对极端天气气候事件影响的传统方法进行了系统比较。总体而言，格兰杰因果归因方法能基于观测数据快速构建结论，无需依赖计算密集型动力实验。这类分析支持快速归因，在极端天气事件发生后具有重要应用价值，当与珀尔因果归因相结合时，能为人为气候变化提供多维度证据。当不同方法论得出相似结论时，归因结果的可信度将显著提升。展望未来，我们呼吁检测与归因学界采用融合格兰杰与珀尔因果优势的混合方法，以推进气候变化归因研究。