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tokens: (McCourt et al. 2018 ; Mandelker et al. 2019 ) and by estimates of cool-phase cloud sizes inferred from measured column densities and derived number densities (e.g. Pieri et al. 2014 ; Crighton et al. 2015 ; Stern et al. 2016 ). Furthermore, with current computational capabilities it is infeasible to maintain even kpc-scale resolution throughout volumes that are tens of Mpc on a side, and standard Lagrangian or mesh refinement schemes will not automatically resolve the regions of the CGM where gas phases interact. Physical processes in addition to radiative hydrodynamics, such as thermal conduction, viscosity, and magnetic fields, may also have significant effects on cloud evolution (Marcolini et al. 2005 ; Orlando et al. 2005 ; Vieser Hensler 2007 ; McCourt et al. 2015 ; Armillotta, Fraternali Marinacci 2016 ; Brüggen Scannapieco 2016 ; Armillotta et al. 2017 ; Li et al. 2020 ), but they are rarely incorporated self-consistently in cosmological simulations. In the approach proposed here, we eject wind particles as in previous simulations but follow their evolution and interaction with the ambient CGM using an analytic subgrid model. We model the gas in each wind particle as a collection of cold clouds, and we calculate the exchange of mass, momentum, energy, and metals between these clouds and the surrounding CGM gas. A wind particle loses mass as it evolves, and it is dissolved when its mass falls below some threshold, or when its velocity and physical properties sufficiently resemble the surrounding gas, or when it rejoins a galaxy and contributes its remaining mass and metals to the ISM. This general method can be implemented in cosmological simulations that use smoothed particle hydrodynamics (SPH) or Eulerian or Lagrangian mesh codes. In future work, we will present results from implementing this Physically Evolved Wind (PhEW) model in cosmological simulations with the gizmo hydrodynamics code (Hopkins Raives 2016 ), employing the star formation and feedback recipes described by Davé et al. ( 2019 ) and the wind launch prescriptions described by Huang et al. ( 2020 ), which are themselves tuned to reproduce outflows in the FIRE simulations (Muratov et al. 2015 ). In this paper, we present the wind model itself. This model is based on results from very high resolution simulations of the ‘cloud-crushing problem’, which examine the evolution of an individual cold clouds moving supersonically relative to an ambient, hotter flow (see Banda-Barragán et al. 2019 for a recent compilation of such simulations). We concentrate in particular on the cloud-crushing simulations of Scannapieco Brüggen ( 2015 , hereafter SB15 ), which do not include thermal conduction, and the simulations of Brüggen Scannapieco ( 2016 , hereafter BS16 ), which do. These simulations model idealized situations with mass and spatial resolutions far higher than that achieved in any galaxy formation or cosmological hydrodynamic simulations. PhEW provides a method to