  Scientific and technological advances in numerical modelling have improved
the quality of climate predictions over recent decades, but predictive skill
remains limited in many aspects. Extreme events such as heat and cold waves,
droughts, heavy rain and storms are particularly challenging to predict
accurately due to their rarity and non-linear chaotic nature, and because of
model limitations. However, recent studies have shown that predictive skill of
extremes can be increased when using more sophisticated approaches, indicating
that there might be systemic predictability that is not being leveraged.
Recently, numerous studies have been devoted to the exploitation of Artificial
Intelligence (AI) to study the predictability and make predictions of weather
and climate. AI techniques have shown great potential to improve the prediction
of extreme events and uncover their links to large-scale and local drivers.
Machine and deep learning, causal discovery, explainable AI, are only some of
the approaches that have been tested to both improve our understanding of the
processes underlying predictability and enhance prediction skill of extreme
events. Results are promising, especially for hybrid predictions that combine
the AI, which can reveal and exploit unknown spatio-temporal connections from
data, and climate models, that provide the theoretical foundation and
interpretability of the physical world. On the other hand, challenges are
multiple in many aspects, from data curation to model uncertainty and
generalizability, to the reproducibility of methods and workflows. A few best
practices are identified to increase trust in these novel techniques, and
future perspectives are envisaged for further scientific development.
