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Although the prediction of the Indian Summer Monsoon (ISM) onset is
of crucial importance for water-resource management and agricultural planning on the
Indian sub-continent, the long-term predictability { especially at seasonal time scales
{ is little explored and remains challenging. We propose a method based on artificial
neural networks that provides skilful long-term forecasts (beyond 3 months) of the ISM
onset, although only trained on short and noisy data. It is shown that the meridional
tropospheric temperature gradient in the boreal winter season already contains the
signals needed for predicting the ISM onset in the subsequent summer season. Our
study demonstrates that machine-learning-based approaches can be simultaneously
helpful for both data-driven prediction and enhancing the process understanding of
climate phenomena.