Abstract
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.