Improving subseasonal-to-seasonal heatwave forecast skill in India through adaptive and aggregated intelligence

Heatwaves in particularly tropical and sub-tropical countries impact public health, resilience, and sustainable development; hence, accurate forecasting of heatwaves is of great importance. Sub-seasonal to seasonal (S2S) forecasts offer vital insights into weather patterns weeks in advance, supporting proactive mitigation, especially in heatwave-prone countries like India. Indian heatwaves are associated with extended dry spells during the pre- and post-monsoon periods and are influenced by large-scale climate variability modes. While S2S temperature forecasts generally outperform precipitation forecasts in short lead times, their predictive skill remains limited for heatwave warnings. To overcome these limitations, this study introduces an Adaptive & Aggregated Intelligence (AAI) framework to enhance the operational S2S-based heatwave prediction across India. The AAI framework post-processes raw S2S (real-time sets) outputs, significantly improving temperature anomaly predictions associated with heatwaves. It achieves a probability of detection exceeding 50%, compared to near-zero in unprocessed forecasts, and reduces the false alarm ratio from 1.0 to 0.3, corresponding to approximately 70% confidence in event occurrence. Furthermore, the framework's capability to extend lead times up to six weeks further enhances its operational value. These results demonstrate the effectiveness of AI-driven post-processing in improving S2S forecast's reliability. This framework is particularly useful for sectors like agriculture, water management, and civic awareness.