Global climate change projections are still subject to substantial modelling uncertainties. A variety of Emergent Constraints (ECs) have been suggested to address these uncertainties, but ECs remain heavily debated in the climate science community. Here I will discuss machine learning (ML) ideas for new types of controlling factor analyses as a promising alternative. The principal idea behind these analyses is to use ML to find climate-invariant relationships in historical data, which also hold approximately under strong climate change scenarios. On the basis of existing data archives such as those from the Coupled Model Intercomparison Projects, these climate-invariant relationships can be validated in perfect-model frameworks. From a ML perspective, I argue that such approaches are more promising for three reasons: (a) they can be objectively validated both for past data and future data and (b) they provide more direct - by design physically-plausible - links between historical observations and potential future climates and (c) they can take higher dimensional relationships into account that better characterize the still complex nature of large-scale emerging relationships. I highlight these advantages for two recently published examples in the form of constraints on climate feedback mechanisms (clouds, stratospheric water vapour)