A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons

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Abstract. We present novel explainable deep learning techniques for reconstructing South Asian palaeomonsoon rainfall over the last 500 years, leveraging long instrumental precipitation records and palaeoenvironmental datasets from South and East Asia to build two types of models: dense neural networks (“regional models”) and convolutional neural networks (CNNs). The regional models are trained individually on seven regional rainfall datasets, and while they capture decadal-scale variability and significant droughts, they underestimate inter-annual variability. The CNNs, designed to account for spatial relationships in both the predictor and target, demonstrate higher skill in reconstructing rainfall patterns and produce robust spatiotemporal reconstructions. The 19th and 20th centuries were characterised by marked inter-annual variability in the monsoon, but earlier periods were characterised by more decadal- to centennial-scale oscillations. Multidecadal droughts occurred in the mid-17th and 19th centuries, while much of the 18th century (particularly the early part of the century) was characterised by above-average monsoon precipitation. Extreme droughts tend to be concentrated in southern and western India and often coincide with recorded famines. The years following large volcanic eruptions are typically marked by significantly weaker monsoons, but the sign and strength of the relationship with the El Niño–Southern Oscillation (ENSO) vary on centennial timescales. By applying explainability techniques, we show that the models make use of both local hydroclimate and synoptic-scale dynamical relationships. Our findings offer insights into the historical variability of the Indian summer monsoon and highlight the potential of deep learning techniques in palaeoclimate reconstruction.