Subimal Ghosh

TL;DR: Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India, driven by an increasing variability of the low-level monsoon westerlies over the Arabian Sea.

TL;DR: In this paper, a methodology of statistical downscaling based on sparse Bayesian learning and relevance vector machine (RVM) is presented to model streamflow at river basin scale for monsoon period (June, July, August, September).

TL;DR: In this paper, the authors use extreme value theory to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability, and show that when generalized extreme value theories are applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches.

TL;DR: The results suggest that future climate warming will lead to substantial increases in heat-related mortality, particularly in developing low-latitude countries, such as India, where heat waves will become more frequent and populations are especially vulnerable to these extreme temperatures.

TL;DR: In this article, the authors compared the performance of models available in CMIP5 and CMIP6 consortium and their multi-model average (MMA) and found a significant improvement in model performance in capturing the spatiotemporal pattern of monsoon over Indian landmass, especially in the Western Ghats and North-east foothills of Himalayas.