A key concern with anthropogenic climate change is that continued warming may trigger transitions in tipping systems. These are Earth system components where small additional climate changes cause large, often irreversible, shifts in state. Such nonlinear transitions could have profound ecological and societal consequences, yet the global warming thresholds at which they occur remain highly uncertain. Current estimates rely mainly on Earth System Models (ESMs), but large inter-model differences undermine confidence in projected thresholds and complicate adaptation and mitigation planning.</p> <p>Alternatively, Early Warning Systems (EWSs) infer proximity to tipping directly from observations, seeking statistical signatures that emerge before transitions. Most existing EWSs rely on generic indicators, such as critical slowing down, making limited use of process understanding. We argue that further progress, or refinement of existing EWSs, will require more process-informed rather than solely generic EWSs, where statistical indicators are guided by the dynamics of the Earth system component under investigation.</p> <p>Earth Observation (EO) offers combining its extensive spatial coverage of, for instance, land ecosystem state with parallel observations of key environmental drivers. EO enables space-for-time approaches that exploit spatial variations and how they may change with proximity to a tipping point. Complementary datasets, such as meteorological records, describe the forcing behind observed EO variability, which may help explain the behaviour of candidate EWSs that incorporate satellite measurements. Combined with reduced dynamical-system representations or ESM process descriptions, these datasets offer a foundation for more targeted and informative EWSs.</p> <p>We review existing approaches to EWS development from climate science and related disciplines, then present theoretical hypotheses and conceptual frameworks for process-informed EWSs. Central to many suggestions is the proposition that the structure of an optimal EWS should reflect the governing dynamics of the underlying climate system component. We further argue that this relationship is bidirectional. Observations can enhance EWSs, which in turn could constrain ESM parameterisation and structure, improving projections by these models of future tipping behaviour. We propose that integrating EO, process understanding, dynamical systems theory, and ESM improvement offers a promising pathway to more robust EWSs and, ultimately, more reliable prediction of climate tipping risks.