  In this paper three customised Artificial Intelligence (AI) frameworks,
considering Deep Learning (convolutional neural networks), Machine Learning
algorithms and data reduction techniques are proposed, for a problem of
long-term summer air temperature prediction. Specifically, the prediction of
average air temperature in the first and second August fortnights, using input
data from previous months, at two different locations, Paris (France) and
C\'ordoba (Spain), is considered. The target variable, mainly in the first
August fortnight, can contain signals of extreme events such as heatwaves, like
the mega-heatwave of 2003, which affected France and the Iberian Peninsula.
Thus, an accurate prediction of long-term air temperature may be valuable also
for different problems related to climate change, such as attribution of
extreme events, and in other problems related to renewable energy. The analysis
carried out this work is based on Reanalysis data, which are first processed by
a correlation analysis among different prediction variables and the target
(average air temperature in August first and second fortnights). An area with
the largest correlation is located, and the variables within, after a feature
selection process, are the input of different deep learning and ML algorithms.
The experiments carried out show a very good prediction skill in the three
proposed AI frameworks, both in Paris and C\'ordoba regions.
