  Stochastic generators are useful for estimating climate impacts on various
sectors. Projecting climate risk in various sectors, e.g. energy systems,
requires generators that are accurate (statistical resemblance to
ground-truth), reliable (do not produce erroneous examples), and efficient.
Leveraging data from the North American Land Data Assimilation System, we
introduce TemperatureGAN, a Generative Adversarial Network conditioned on
months, locations, and time periods, to generate 2m above ground atmospheric
temperatures at an hourly resolution. We propose evaluation methods and metrics
to measure the quality of generated samples. We show that TemperatureGAN
produces high-fidelity examples with good spatial representation and temporal
dynamics consistent with known diurnal cycles.
