What does this research mean for the field?

The field of extreme rainfall prediction is transitioning from deterministic deep learning models to hybrid, physics-informed, and uncertainty-aware systems, though significant challenges regarding physical consistency, data scarcity, and standardized evaluation remain. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Synthesize recent advances in artificial intelligence for extreme rainfall prediction and examine key requirements for operational, physics-informed forecasting systems.

August 30, 2026Frontiers in Artificial IntelligenceOpen Access

Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art

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Authors

BMBraiton MukhalelaUniversity of KwaZulu-Natal SVSerestina ViririUniversity of Pretoria DNDavid NdziUniversity of Portsmouth

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Implication

Systematic review reveals a transition toward hybrid, physics-informed models for extreme rainfall prediction, highlighting the need for uncertainty-aware forecasting frameworks.

Key Points

Synthesize recent advances in artificial intelligence for extreme rainfall prediction and examine key requirements for operational, physics-informed forecasting systems.
Conducted a systematic review of literature published between 2020 and 2026 using predefined screening criteria and quality assessment protocols.
Analyzed an evidence base of 139 studies across deep learning architectures, multimodal data fusion, forecasting horizons, and uncertainty quantification.
Identified an ongoing paradigm shift from deterministic deep learning models toward hybrid, physics-informed, and uncertainty-aware operational forecasting frameworks.
Highlighted persistent methodological bottlenecks across current systems, notably data scarcity, extreme class imbalance, limited transferability, and a lack of standardized physical evaluation metrics.

Cite This Study

Mukhalela et al. (2026) studied this question.

synapsesocial.com/papers/6a93f00a6c1a8fb52e79c00f https://doi.org/10.3389/frai.2026.1908848

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Implication

Systematic review reveals a transition toward hybrid, physics-informed models for extreme rainfall prediction, highlighting the need for uncertainty-aware forecasting frameworks.

Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art

Discussion

Member takes

Implication

Key Points

Cite This Study

Deep learning techniques for extreme rainfall prediction: a review of state-of-the-art

Discussion

Member takes

Implication

Key Points

Cite This Study

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