Short‐Term Agricultural Drought Prediction Using Explainable Data‐Driven Models and Quantified Uncertainty

Accurate short‐term prediction of agricultural drought is critical for sustainable crop management and climate risk mitigation in drought‐prone regions. This study evaluates the effectiveness of the Standardized Precipitation Evapotranspiration Index (SPEI) as a key lagged predictor of agricultural drought in the Palakkad district of Kerala, India. Multiple machine learning and deep learning models were evaluated using lagged values of SPEI and the Standardized Soil Moisture Index (SSI) to predict SSI at 1–3‐month lead times. An additional model configuration incorporated global climatic indices to assess their potential influence on regional drought dynamics. Predictive uncertainty was quantified using Quantile Regression (QR), and model interpretability was evaluated through SHapley Additive exPlanations (SHAP) to identify the primary drivers of drought evolution. Unlike most short‐term drought studies, this work jointly integrates predictive uncertainty and explainable artificial intelligence to enhance operational reliability. Results indicate that the Artificial Neural Network (ANN) consistently achieved the highest predictive accuracy, with R 2 values reaching 0.84 at 1‐month lead time, decreasing to 0.53 at 2‐month and 0.26 at 3‐month. Root mean Square Error (RMSE) ranged between 0.50 and 0.59 at 1‐month lead time, but increased progressively at longer lead times. The Convolutional Neural Network (CNN) demonstrated comparable performance, with R 2 values ranging from 0.77 to 0.82 across different grid points at the 1‐month lead time. Uncertainty analysis demonstrated high coverage reliability (PICP ≈ 0.88–0.89) across ANN models, with increasing interval width at longer lead times. SHAP analysis confirms that local lagged hydroclimatic variables dominate near‐term drought prediction, while climatic indices provide only marginal improvements. The integration of uncertainty quantification and interpretability enhances the operational utility of these models, offering a transparent and scientifically robust framework for short‐term drought prediction, early warning applications and climate‐resilient agricultural management.