  Seasonal forecasting is a crucial task when it comes to detecting the extreme
heat and colds that occur due to climate change. Confidence in the predictions
should be reliable since a small increase in the temperatures in a year has a
big impact on the world. Calibration of the neural networks provides a way to
ensure our confidence in the predictions. However, calibrating regression
models is an under-researched topic, especially in forecasters. We calibrate a
UNet++ based architecture, which was shown to outperform physics-based models
in temperature anomalies. We show that with a slight trade-off between
prediction error and calibration error, it is possible to get more reliable and
sharper forecasts. We believe that calibration should be an important part of
safety-critical machine learning applications such as weather forecasters.
