Statistical emulators of globally gridded crop models are designed to provide decision-makers with a far less computationally intensive way to assess the impact of climate change on crop yields. In a previous paper (Blanc, 2017) focused on four major rain-fed breadbasket crops—maize, rice, soybean and wheat—the author developed a new set of crop yield emulators and showed that they could produce results comparable to those generated by an ensemble of globally gridded crop model simulations upon which they were trained. This study advances statistical emulators to provide an accessible tool to assess the impact of climate change on irrigated crop yields and irrigation water withdrawals, while accounting for crop modeling uncertainty. Together with the 2017 study, this research enables decision-makers to estimate the impact of climate change on, separately, rain-fed and irrigated crops, resulting in a more comprehensive assessment of the impact of climate change on agriculture.