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Convolutional Neural Network‐Based Insights Into Extreme Precipitation Regional Dynamics Over Central Africa Using Moisture Flux Patterns

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Analysis reveals complex moisture flux patterns driving extreme rainfall events in Central Africa, highlighting regional variability.

The CNN model identifies 95% of extreme rainfall events using moisture flux inputs, enhancing predictions.
Layer-wise relevance propagation shows how moisture dynamics relate to strong rainfall and drought events.
Observations indicate an increase in extreme precipitation moisture flux patterns over the last two decades, linking to rising rainfall.
Distinct zonal moisture transport patterns reveal dynamic differences between extreme and non-extreme rainfall events.

Mouassom et al. (2025) studied this question.

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