  Emulation of the Global (sky-averaged) 21-cm signal with neural networks has
been shown to be an essential tool for physical signal modelling. In this paper
we present globalemu, a Global 21-cm signal emulator that uses redshift as a
character defining variable alongside a set of astrophysical parameters to
estimate the signal brightness temperature. Combined with physically-motivated
data pre-processing this makes for a reliable and fast emulator that is
relatively insensitive to the network design. globalemu can emulate a high
resolution signal in 1.3 ms in comparison to 133 ms, a factor of 102
improvement, when using the existing public state of the art 21cmGEM. We
illustrate, with the standard astrophysical models used to train 21cmGEM, that
globalemu 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$10
per cent the expected noise of the Radio Experiment for the Analysis of Cosmic
Hydrogen (REACH)). The models are parameterised by the star formation
efficiency, $f_*$, 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-ray
spectral energy density, $\alpha$ and $\nu_\mathrm{min}$, and the mean free
path of ionizing photons, $R_\mathrm{mfp}$. globalemu provides a flexible
framework for easily emulating updated simulations of the Global signal and in
addition the neutral fraction history. The emulator is pip installable and
available at: \url{https://github.com/htjb/globalemu}. globalemu will be used
extensively by the REACH collaboration.
