  Global warming leads to the increase in frequency and intensity of climate
extremes that cause tremendous loss of lives and property. Accurate long-range
climate prediction allows more time for preparation and disaster risk
management for such extreme events. Although machine learning approaches have
shown promising results in long-range climate forecasting, the associated model
uncertainties may reduce their reliability. To address this issue, we propose a
late fusion approach that systematically combines the predictions from multiple
models to reduce the expected errors of the fused results. We also propose a
network architecture with the novel denormalization layer to gain the benefits
of data normalization without actually normalizing the data. The experimental
results on long-range 2m temperature forecasting show that the framework
outperforms the 30-year climate normals, and the accuracy can be improved by
increasing the number of models.
