A comparative evaluation of machine learning algorithms for statistical downscaling of monthly mean temperature data over a European region

Climate change is the most vital environmental change that has already started to affect many ecosystems. It is caused by greenhouse gas emissions which are increasing since the pre-industrial era, and populated areas become more vulnerable to disasters due to climate change. It has never been more crucial to model the climate effects on local regions. Organizations like Intergovernmental Panel on Climate Change (IPCC) use global climate models (GCMs) to project future changes in climate on a continental scale. Although these models are becoming more accurate, downscaling these models to smaller scales is an important task that is studied by climate scientists. The two main downscaling methods are dynamical and statistical downscaling. Statistical downscaling studies are more reachable and important to develop when compared to dynamical downscaling due to its lower costs. The use of machine learning algorithms in statistical downscaling is a new area. Studies that implement machine learning to make local scale projections of surface temperature are numbered. In this paper, four different machine learning algorithms were tested on downscaling of two different surface temperature datasets over a European region with different resolutions. The best performing algorithm was also tested augmenting elevation data. The results show that Gaussian process regression performs the best with MAE of 0.04 - 0.51 as compared to the other machine learning algorithms tested. In conclusion, machine learning algorithms such as Gaussian process regression can be a suitable approach when downscaling spatial monthly mean surface temperature data.