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.

翻译：理解季节性气候条件对于更好地管理水资源、能源和农业等资源至关重要。近年来，利用人工智能方法进行气候研究引起了广泛兴趣。本文提出了一种前沿的深度学习模型（UNet++），该模型采用最先进的全球CMIP6模式进行训练，利用ERA5再分析数据集对全球温度进行提前一个月的预测。ERA5数据集还用于微调以及验证数据集的性能分析。我们使用了三种不同设置（CMIP6；CMIP6 + 高程；CMIP6 + 高程 + ERA5微调），结合UNet和UNet++算法，共构建了六种不同模型。对每种模型采用了14种不同的时序与非时序时间设置。平均绝对误差（MAE）分析表明，采用CMIP6与高程及ERA5微调的UNet++模型在“第三年第二个月”时间设置下表现最佳，MAE为0.7。在验证数据集上对ERA5数据值与相应AI模型预测进行回归分析，斜率和$R^2$值接近1，表明两者具有良好的一致性。2016年至2021年间，AI模型的预测显著优于CMIP6集合平均。两种模型对夏季月份的预测均比冬季月份更准确。