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Many rare weather events, including hurricanes, droughts, and floods,dramatically impact human life. To accurately forecast these events andcharacterize their climatology requires specialized mathematical techniques tofully leverage the limited data that are available. Here we describe\emph{transition path theory} (TPT), a framework originally developed formolecular simulation, and argue that it is a useful paradigm for developingmechanistic understanding of rare climate events. TPT provides a method tocalculate statistical properties of the paths into the event. As an initialdemonstration of the utility of TPT, we analyze a low-order model of suddenstratospheric warming (SSW), a dramatic disturbance to the polar vortex whichcan induce extreme cold spells at the surface in the midlatitudes. SSW eventspose a major challenge for seasonal weather prediction because of their rapid,complex onset and development. Climate models struggle to capture the long-termstatistics of SSW, owing to their diversity and intermittent nature. We use astochastically forced Holton-Mass-type model with two stable states,corresponding to radiative equilibrium and a vacillating SSW-like regime. Inthis stochastic bistable setting, from certain probabilistic forecasts TPTfacilitates estimation of dominant transition pathways and return times oftransitions. These "dynamical statistics" are obtained by solving partialdifferential equations in the model's phase space. With future application tomore complex models, TPT and its constituent quantities promise to improve thepredictability of extreme weather events, through both generation andprincipled evaluation of forecasts.

Path properties of atmospheric transitions: illustration with a low-order sudden stratospheric warming model