S2S Forecast Skill Assessment for Summer Monsoon Drought Warning

Persistent extreme weather anomalies lasting several weeks to months can lead to drought and compound dry–hot extremes, posing serious socio-economic risks in the Indian monsoon region. Although subseasonal-to-seasonal (S2S) prediction systems have advanced, the extent to which these models represent drought-relevant hydroclimatic variability over India has not been adequately quantified. Here, we focus on evaluating the hindcast quality of weekly accumulated precipitation and temperature from multiple S2S models with lead times up to six weeks during the JJAS season over India. These model hindcast outputs and IMD observations are regridded to a common 0.5° resolution and analyzed using deterministic forecast skill metrics at various lead times, statistical bias correction is then applied to isolate systematic model errors, followed by SPI-based drought diagnostics, and compound dry–hot extreme indices are derived and computed. The analysis reveals modest forecast skill at early lead times, followed by a systematic decline as lead time increases, with precipitation predictability deteriorating more rapidly than temperature predictability. Although the models generally capture the large-scale spatial distribution of drought-prone regions, they significantly underestimate the frequency and spatial extent of compound dry–hot conditions, exhibiting pronounced regional dependence across India. These results highlight key limitations and identify opportunities to enhance subseasonal drought early-warning systems. 

Keywords: Subseasonal-to-seasonal predictability, Indian Summer Monsoon, Drought, Climate extremes, Hindcast evaluation