Estimating risk of future impacts as driven by climate hazards, especially extremes, requires a robust characterization of these hazards’ statistics. Earth System Models enable research on the earth's future climate under alternative plausible scenarios, but are extremely costly to run, providing only a limited number of scenarios/ensemble members. Earth System Model output emulators aim to supplement ESMs, allowing computationally efficient generation of new scenarios or realizations of internal variability, the latter being particularly important when studying extremes. In this paper we propose an approach to generating realistic time series of global daily precipitation fields requiring orders of magnitude less computation, using a conditional generative adversarial network (GAN) as an emulator of an Earth System Model (ESM). Specifically, we present a GAN that emulates daily precipitation output from a fully coupled ESM, conditioned on monthly mean values. The GAN is trained to produce spatio-temporal samples: 28 days of precipitation in a 92 × 144 regular grid discretizing the globe. We evaluate the generator by comparing generated and real distributions of precipitation metrics including average precipitation, average fraction of dry days, average dry spell length, and average precipitation above the 90th percentile, finding that the generated samples closely match those of real data, even when conditioned on climate scenarios never seen during training.