Spatial heterogeneity of cloud hydrometeors during extreme monsoon rainfall over India

Abstract Cloud feedback mechanisms remain a major source of uncertainty in weather prediction, particularly over monsoon dominated regions like India where rainfall exhibits strong spatial and temporal variability. This study analyzed Indian Meteorological Department (IMD) daily gridded rainfall, and European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) reanalysis datasets for the monsoon months June-September (JJAS) during 2014–2023, to examine how cloud microphysical properties and processes modulate extreme heavy rainfall across the three monsoon convective zones of India - Western Ghats, Central India, and North-East India. Rainfall categorization, cloud hydrometeor profiles, and cloud microphysical process estimated through Tao’s algorithm were used to investigate the vertical profiles of cloud liquid, ice, and rainwater content, and their linkages with dynamical and thermodynamical conditions. The present work shows pronounced spatial heterogeneity in cloud microphysical properties and processes, with distinct variation in magnitude and vertical distribution, indicating region-specific cloud microphysical characteristics and processes. These differences translate into region-specific microphysical process rates (rain condensation, evaporation, and deposition) which directly influence the formation and intensity of Extreme Heavy rainfall (EH). Deeper convection over North-East region is shown to result in stronger condensation and EH. However, over Western Ghats a higher amount of lower-level liquid water content is found to drive EH. Furthermore, over Central India the high ice water content found in the upper troposphere during EH events is found to form due to aggregation rather than vapor deposition. Such contrasting cloud structures explain the uneven spatial distribution of extreme rainfall across India. This study underscores that spatial heterogeneity in cloud microphysics is fundamental to understanding extreme heavy rainfall variability and strengthening climate resilience in monsoon-affected regions.