  Large-scale numerical simulations ($\gtrsim 500\rm{Mpc}$) of cosmic
reionization are required to match the large survey volume of the upcoming
Square Kilometre Array (SKA). We present a multi-fidelity emulation technique
for generating large-scale lightcone images of cosmic reionization. We first
train generative adversarial networks (GAN) on small-scale simulations and
transfer that knowledge to large-scale simulations with hundreds of training
images. Our method achieves high accuracy in generating lightcone images, as
measured by various statistics with mostly percentage errors. This approach
saves computational resources by 90% compared to conventional training methods.
Our technique enables efficient and accurate emulation of large-scale images of
the Universe.
