Climate models encode our knowledge of the Earth system, enabling research on the earth’s future climate under alternative assumptions of how human-driven climate forcings, especially greenhouse gas emissions, will evolve. One important use of climate models is to estimate the impacts of climate change on natural and societal systems under these different possible futures. Unfortunately, running many simulations on existing models is extremely computationally expensive. These computational demands are particularly problematic for characterizing extreme events, which are rare and thus demand numerous simulations in order to precisely estimate the relevant climate statistics. In this paper we propose an approach to generating realistic global precipitation 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 92x144 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 the generated samples to closely match those of real data, even when conditioned on climate scenarios never seen during training.