To achieve long-term climate change goals, such as limiting global warming to 1.5 or 2°C, there must be a global effort to decide and act upon effective but realistic emission scenarios. This requires an understanding of the consequences of various scenarios and the climate response associated with these. In particular, different quantities and types of emissions, such as greenhouse gases and aerosols, are responsible for diverse changes in climate on a global and local scale. However, state-of-the-art climate models required for long-term climate change projections are computationally expensive. Here I will introduce a novel machine learning approach, which uses a unique dataset of existing simulations to learn relationships between short- and long-term temperature responses to different emission scenarios. We have explored several statistical techniques for this supervised learning task and here I present predictions made with Ridge regression and Gaussian process regression. These methods outperform pattern scaling, a standard simplified approach for estimating spatial patterns of temperature response. I will highlight challenges and opportunities for data-driven climate model emulation, especially concerning the incorporation of even larger model datasets in the future.