Assessment of embedded risk in precipitation tails over India through concentration profiles: a multi-model assessment from CMIP6 experiments - Sukhsehaj Kaur; Neha Gupta; Sagar Rohidas Chavan (Natural Hazards, 2026)

Ligilactobacillus salivarius and Limosilactobacillus reuteri improve growth and intestinal health in broilers via modulating gut microbiota and immune response - Neha Chavan; Mahesh Gupta; Krushna Bibichand Bahiram; Jayant Pandurang Korde; Mukund Kadam; Atul Dhok; Sachin Kumar (Research in Veterinary Science, 2025)

Determining the impact of anthropogenic activities and climate change on landslide susceptibility for the Himalayan region - Ankit Tyagi; Neha Gupta; Reet Kamal Tiwari; Naveen James; Sagar Rohidas Chavan (Natural Hazards, 2025)

Assessing future changes in daily precipitation tails over India: insights from multimodel assessment of CMIP6 GCMs - Neha Gupta; Sagar Rohidas Chavan (Theoretical and Applied Climatology, 2024)

Assessment of changes in monthly streamflow using innovative polygon trend analysis in the South Indian Rivers - Neha Gupta; Sagar Rohidas Chavan (Arabian Journal of Geosciences, 2023)

A comprehensive decision support system for the characterization of probability distribution tails for daily precipitation - Neha Gupta; Sagar Rohidas Chavan (Journal of Hydrology, 2023)

Investigating the tail behaviour and associated risk with daily discharges in South Indian Rivers - Neha Gupta; Sagar Rohidas Chavan (Stochastic Environmental Research and Risk Assessment, 2023)

Characterizing the tail behaviour of daily precipitation probability distributions over India using the obesity index - Neha Gupta; Sagar Rohidas Chavan (International Journal of Climatology, 2022)
Extreme precipitation events lie in the upper part of the probability distribution of daily precipitation data, that is, the tail. Depending upon the tail behaviour, various probability distributions are partitioned into heavy‐tailed and light‐tailed distributions. Heavy‐tails tend to approach zero less rapidly than light‐tails, signifying a higher frequency of occurrences for extreme precipitation events. Prediction of extreme precipitation depends on how reliably the distribution tail is modelled. Tail behaviour can be studied by graphical as well as threshold‐based fitting approaches. However, the graphical methods are time‐consuming and do not provide quantitative comparisons between two distributions, whereas the threshold‐based approaches possess limitations such as ambiguity in selecting an optimum threshold for demarcation of the tail. This article assesses the utility of a simple empirical index, that is, the “ Obesity Index ” (OB) to discern the probability distributions of daily gridded precipitation data with a resolution of 0.25° for historical (1951–2004) and future (2006–2099) periods over India into light‐ and heavy‐tailed. The OB‐based approach is an easy‐to‐use empirical approach that can quantitatively diagnose the heaviness of distribution tails without assuming any threshold for segregating the tails. Future projections of daily precipitation were obtained by downscaling simulations of the Coordinated Regional Climate Downscaling Experiment. Subsequently, a comparative analysis between the OB‐based approach and threshold‐based approaches by Nerantzaki and Papalexiou and Papalexiou et al. was conducted. Finally, the application of the OB‐based approach is extended to characterize daily precipitation in Indian Meteorological Subdivisions. Furthermore, we explored the dependence of the OB on the elevation of grids. Results indicated the applicability of heavy‐tailed distributions in the representation of daily precipitation over India and suggest an OB‐based approach as a good alternative diagnostic tool for assessing tail behaviour.