Accurate prediction of flowering time under climate extremes is essential for developing resilient chickpea cultivars. This study proposes a heat-wave-aware extension of the degreeday model using a Generalized Additive Mixed Model (GAMM) framework. The model integrates cumulative growing degree days, heat-wave intensity, and a temporal kernel that emphasizes heat stress occurring near floral initiation. Using multi-year field data and ERA5-Land weather inputs, the proposed model achieved a root mean squared error (RMSE) of 4.76 days and a mean absolute error (MAE) of 3.62 days, improving prediction accuracy by over 15% compared to a conventional linear degreeday model (RMSE: 5.63 days). Partial dependence analysis revealed a saturating delay effect beyond six heat-wave days, particularly when stress occurred within 10−15% of cumulative GDD around floral initiation. The model generalized well across cultivars and site-years, providing a robust and interpretable tool for phenology modeling under heat stress. These findings offer practical implications for climate-adaptive chickpea breeding and management.