Abstract Developing methods for detecting tipping phenomena at an early stage is an important problem in various fields such as ecology, medicine, and economics. A tipping phenomenon is characterized by a rapid transition resulting from the accumulation of small parameter changes, and is known to be related to the bifurcation theory of dynamical systems. However, few studies have examined how nonlinear properties near bifurcation points affect early warning signals (EWSs) performance. In this study, we apply the Koopman operator, which describes the time evolution of dynamical systems in an infinite-dimensional function space, to generalize stochastic resilience the theoretical basis of EWSs such as variance-based ones. As a result, we develop a novel signal capable of more accurately predicting tipping events by separately isolating stochastic fluctuations induced by noise and contributions from a continuous spectrum emerging immediately above tipping points. Our experiments indicate that our proposed method provides robust early warning detection across diverse datasets and is notably resilient to observation noise, often performing competitively with conventional indicators.
Miyauchi et al. (2026) studied this question.