  Understanding seasonal climatic conditions is critical for better management
of resources such as water, energy and agriculture. Recently, there has been a
great interest in utilizing the power of artificial intelligence methods in
climate studies. This paper presents a cutting-edge deep learning model
(UNet++) trained by state-of-the-art global CMIP6 models to forecast global
temperatures a month ahead using the ERA5 reanalysis dataset. ERA5 dataset was
also used for finetuning as well performance analysis in the validation
dataset. Three different setups (CMIP6; CMIP6 + elevation; CMIP6 + elevation +
ERA5 finetuning) were used with both UNet and UNet++ algorithms resulting in
six different models. For each model 14 different sequential and non-sequential
temporal settings were used. The Mean Absolute Error (MAE) analysis revealed
that UNet++ with CMIP6 with elevation and ERA5 finetuning model with "Year 3
Month 2" temporal case provided the best outcome with an MAE of 0.7. Regression
analysis over the validation dataset between the ERA5 data values and the
corresponding AI model predictions revealed slope and $R^2$ values close to 1
suggesting a very good agreement. The AI model predicts significantly better
than the mean CMIP6 ensemble between 2016 and 2021. Both models predict the
summer months more accurately than the winter months.
