  Large sets of matter density simulations are becoming increasingly important
in large-scale structure cosmology. Matter power spectra emulators, such as the
Euclid Emulator and CosmicEmu, are trained on simulations to correct the
non-linear part of the power spectrum. Map-based analyses retrieve additional
non-Gaussian information from the density field, whether through human-designed
statistics such as peak counts, or via machine learning methods such as
convolutional neural networks. The simulations required for these methods are
very resource-intensive, both in terms of computing time and storage. Map-level
density field emulators, based on deep generative models, have recently been
proposed to address these challenges. In this work, we present a novel mass map
emulator of the KiDS-1000 survey footprint, which generates noise-free
spherical maps in a fraction of a second. It takes a set of cosmological
parameters $(\Omega_M, \sigma_8)$ as input and produces a consistent set of 5
maps, corresponding to the KiDS-1000 tomographic redshift bins. To construct
the emulator, we use a conditional generative adversarial network architecture
and the spherical CNN $\texttt{DeepSphere}$, and train it on N-body-simulated
mass maps. We compare its performance using an array of quantitative comparison
metrics: angular power spectra $C_\ell$, pixel/peaks distributions, $C_\ell$
correlation matrices, and Structural Similarity Index. Overall, the average
agreement on these summary statistics is $<10\%$ for the cosmologies at the
centre of the simulation grid, and degrades slightly on grid edges. Finally, we
perform a mock cosmological parameter estimation using the emulator and the
original simulation set. We find good agreement in these constraints, for both
likelihood and likelihood-free approaches. The emulator is available at
https://tfhub.dev/cosmo-group-ethz/models/kids-cgan/1.
