  Climate models are an important tool for the assessment of prospective
climate change effects but they suffer from systematic and representation
errors, especially for precipitation. Model output statistics (MOS) reduce
these errors by fitting the model output to observational data with machine
learning. In this work, we explore the feasibility and potential of deep
learning with convolutional neural networks (CNNs) for MOS. We propose the CNN
architecture ConvMOS specifically designed for reducing errors in climate model
outputs and apply it to the climate model REMO. Our results show a considerable
reduction of errors and mostly improved performance compared to three commonly
used MOS approaches.
