Abstract; Anthropogenic climate change is unfolding rapidly, yet its regional manifestation can be obscured by internal variability. A primary goal of climate science is to identify the externally forced climate response from among the noise of internal variability. Separating the forced response from internal variability can be addressed in climate models by using a large ensemble to average over different possible realizations of internal variability. However, with only one realization of the real world, it is a major challenge to isolate the forced response directly in observations. In the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP), contributors used existing and newly developed statistical and machine learning methods to estimate the forced response over 1950–2022 within individual realizations of the climate system. Participants used neural networks, linear inverse models, fingerprinting methods, and low-frequency component analysis, among other approaches. These methods were trained using large ensembles from multiple climate models and then applied to observations. Here, we evaluate method performance within large ensembles and investigate the estimates of the forced response in observations. Our results show that many different types of methods are skillful for estimating the forced response in climate models, though the relative skill of individual methods varies depending on the variable and evaluation metric. Methods with comparable skill in models can give a wide range of estimates of the forced response pattern in observations, illustrating the epistemic uncertainty in forced response estimates. ForceSMIP gives new insights into the forced response in observations, its uncertainty, and methods for its estimation.; ; Significance Statement; The Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP) aims to reduce uncertainty in estimates of the climate response to anthropogenic and other external forcing and to evaluate statistical and machine learning methods designed to estimate the forced response from individual realizations of the climate system. New and existing statistical and machine learning methods are evaluated within climate models, for which the forced response is known. Applying these methods to observations gives an estimate of the real-world forced response. The observational forced response estimate agrees with climate models on the large-scale features, but it also shows discrepancies that give insights into responses that may not be simulated well by climate models. In some regions with large internal variability, such as the North Atlantic Ocean, it remains difficult to determine the relative contributions of anthropogenic forcing and internal variability to historical changes.;
