1. The document discusses using explainable neural networks to compare climate model projections and evaluate which climate models best match observations. 2. Temperature maps from observations are input into a neural network trained on climate model data to classify each observation year with a climate model. 3. Layer-wise relevance propagation is used to explain the neural network's classifications and identify differences between climate models, which can help evaluate models, especially in regions with known biases like the Arctic.