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Emulation of the Global (sky-averaged) 21-cm signal with neural networks hasbeen shown to be an essential tool for physical signal modelling. In this paperwe present globalemu, a Global 21-cm signal emulator that uses redshift as acharacter defining variable alongside a set of astrophysical parameters toestimate the signal brightness temperature. Combined with physically-motivateddata pre-processing this makes for a reliable and fast emulator that isrelatively insensitive to the network design. globalemu can emulate a highresolution signal in 1.3 ms in comparison to 133 ms, a factor of 102improvement, when using the existing public state of the art 21cmGEM. Weillustrate, with the standard astrophysical models used to train 21cmGEM, thatglobalemu is almost twice as accurate and for a test set of $\approx1,700$signals we achieve a mean RMSE of 2.52 mK across the band z=7-28 ($\approx$10per cent the expected noise of the Radio Experiment for the Analysis of CosmicHydrogen (REACH)). The models are parameterised by the star formationefficiency, $f_*$, minimum virial circular velocity, $V_c$, X-ray efficiency,$f_X$, CMB optical depth, $\tau$, the slope and low energy cut off of the X-rayspectral energy density, $\alpha$ and $\nu_\mathrm{min}$, and the mean freepath of ionizing photons, $R_\mathrm{mfp}$. globalemu provides a flexibleframework for easily emulating updated simulations of the Global signal and inaddition the neutral fraction history. The emulator is pip installable andavailable at: \url{https://github.com/htjb/globalemu}. globalemu will be usedextensively by the REACH collaboration.

GLOBALEMU: A novel and robust approach for emulating the sky-averaged 21-cm signal from the cosmic dawn and epoch of reionisation