This study explores the applicability of process-oriented diagnostics (POD) for accurately representing convection and understanding of moist processes, which leads to skillful prediction and model development, over tropical regions, focusing on the boreal summer monsoon over the Indian subcontinent. By utilizing the, indigenously developed NCMRWF's high-resolution regional reanalysis, IMDAA and validated using ERA5, the analysis examines large-scale variables such as moisture and temperature, focusing on the warm season (May to October) to understand the instability-convection coupling.

The study employs the Gálvez–Davison Index (GDI) as a key tool to address the limitations of existing stability indices (KI, LI, TTI, CAPE, and CINE), which often exhibit discrepancies in accurately capturing the instability-convection coupling. Focusing on convective breeding grounds, such as Central India (CEN) and the Bay of Bengal (BoB), the results reveal that GDI exhibits a stronger and more coherent association with rainfall intensity and variability when compared with other widely accepted stability indices. While CAPE and KI show reasonable agreement with rainfall evolution, GDI proves to be more reliable in representing deep convection over both CEN and BoB. Further, this study highlights the significance of thermodynamic instability and free-tropospheric moisture in convective growth, emphasizing the role of moisture at lower levels and maximum equivalent potential temperature (θe). The study demonstrates that GDI, which is tightly linked to convective-quasi equilibrium (CQE) offers significant improvements in the evolution of convection, and its integration into forecasting models is recommended to enhance the representation of convective initiation and overall forecast accuracy. Furthermore, the study emphasizes the need for high-resolution reanalysis and coupled models for a better understanding of convection.