Anticipating climate impacts and risks in present or future climates requires predicting the statistics of high-impact weather events at fine-scales. Direct numerical simulations of fine-scale weather are computationally too expensive for many uses. While regression-based (deep-learning or statistical) downscaling of low-resolution climate simulations is several orders of magnitude faster than direct numerical simulations, it suffers from several limitations. These limitations include the tendency to regress to the mean, which produces excessively smooth predictions and underestimates the magnitude of extreme events. Additionally, they also fail to preserve statistical measures that are key for climate research. We use a conditional GAN (c-GAN) architecture to downscale daily precipitation as a Regional Climate Model (RCM) emulator. The c-GAN generates plausible residuals on top of the predictable expectation state produced by a regression-based DL algorithm. The skill of c-GANs is highly sensitive to a hyperparameter known as the weight of the adversarial loss (\(\lambda_{adv\ }\)), and the value of \(\lambda_{adv}\) required for accurate results varies with season and performance metric, casting doubt on the robustness of c-GANs as usually implemented. But, by applying a simple intensity constraint to the loss function, it is possible to obtain robust performance results across \(\lambda_{adv}\) spanning two orders of magnitude. C-GANs are considerably more skillful in capturing climatological statistics including the distribution and spatial characteristics of extreme events. We expect c-GANs with this modification to be readily transferable to other problems and time periods, making them a useful weather generator for representing extreme event statistics in present and future climates.