We present a first application of a fast super resolution convolutional neural network (FSRCNN) approach for downscaling climate simulations. Unlike other SR approaches, FSRCNN uses the same input feature dimensions as the low resolution input. This allows it to have smaller convolution layers, avoiding over-smoothing, and reduced computational costs. We further adapt FSRCNN to feature additional convolution layers after the deconvolution layer, we term FSRCNN-ESM. We use high-resolution (0.25°) monthly averaged model output of five surface variables over a part of North America from the US Department of Energy’s Energy Exascale Earth System Model’s control simulation. These high-resolution and corresponding coarsened low-resolution (1°) pairs of images are used to train the FSRCNN-ESM and evaluate its use as a downscaling approach. We find that FSRCNN-ESM outperforms FSRCNN and other methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes and precipitation.