Summertime heat stress future projections from multi-model mean of 18 CMIP5 models show unprecedented increasing levels in the RCP 4.5 and RCP 8.5 emission scenarios over India. The estimated heat stress is found to have more impact on the coastal areas of India having exposure to more frequent days of extreme caution to danger category along with the increased probability of occurrence. The explicit amount of change in temperature, increase in the duration and intensity of warm days along with the modulation in large scale circulation in future are seemingly connected to the increasing levels of heat stress over India. A decline of 30 to 40% in the work performance is projected over India by the end of the century due to the elevated heat stress levels which pose great challenges to the country policy makers to design the safety mechanisms and to protect people working under continuous extreme hot weather conditions.

India’s climate is semi-arid, and most of the people in this country depend on agriculture. In this article, we present the long-term relationships of the satellite-derived Normalized Difference Vegetation Index (NDVI) with the rainfall, land surface temperature (LST), surface soil moisture (SSM), and the groundwater storage (GWS) during the Kharif (June–October) season of the monsoon core region (MCR) of India from the year 2000 to 2018. Results show that the NDVI could capture the stages of crop phenology and that it undergoes interannual variability characterized by the El Niño Southern Oscillation. A strong dependence of NDVI on SSM and GWS is evidenced by the statistically significant correlation coefficients of +0.89 and +0.88, respectively. A substantial negative correlation coefficient of −0.91 is observed between NDVI and LST. Wavelet coherence spectrum suggests the in-phase relation of NDVI with rainfall, SSM, GWS, and anti-phase connection with LST, with weaker correlations from 2004 to 2008. The results of this study attain significance since the studies on NDVI and hydrologic variables are much less than those on NDVI and climate variables over the MCR of India.

The paper compares the final run of Integrated Multi‐Satellite Retrieval of Global Precipitation Mission (IMERG) products with India Meteorological Department (IMD) gridded data over the Indian land mass during the southwest monsoon period (June–September) in the period 2014–2017. Spatiotemporal variations of the IMERG are evaluated with IMD rainfall by employing different statistical techniques, and the capability of the IMERG is examined using the categorical skill metrics. A region in central India is the focus in order to study the southwest monsoon's static and dynamic characteristics, such as rainfall distribution and monsoon activity, using the IMERG in conjunction with IMD rainfall data sets. The integrated condensation rate (ICR) was estimated using the specific humidity profiles, and it correlated with the IMERG and IMD rainfall. The IMERG is found to be a potential source for adequately reflecting the ground gauge‐gridded data of categorical rainfall amounts, from very light rain (trace–2.4 mm) to very heavy rain (about 115.6–204.4 mm). However, the IMERG does not reflect satisfactorily the extreme heavy rain events (≥ 204.5 mm·day^–1) during the study period. The significant correlation between IMERG/IMD rainfall and the ICR suggests that improved adjustments methods are required for better results when depicting accurate extreme heavy rainfall events by the IMERG.

Present study focuses on rainfall over Western Ghats (WG), a complex topographic region (elevation > 500 m) of India to evaluate and to better understand the satellite behavior in contrast with a flat region (FR) (elevation < 500 m) of central India from 1998 to 2016 using the combinatory data sets of TMPA and IMERG (satellite rainfall estimation). The categorical Intra Seasonal Oscillations (ISO) of Indian summer monsoon (ISM) namely, Madden Julian Oscillation (MJO) and Quasi Bi-Weekly Oscillation (QBWO) are tested in satellite and India Meteorological Department (IMD) gridded rainfall data sets to find out the satellite performance. As the accurate estimation of rainfall from satellites over higher elevation zones is challenging, here we propose a new perspective to select the rainfall products of satellite for better comparison with ground measurements. Considering the satellite’s best capability in detecting the cold clouds resulting from deep convection and its coupling with higher-level circulation, we show that the rainfall from satellites yield fruitful comparison with ground measurements when moist static stability, tropical easterly jet is above the climatological values.

The Paris Agreement and the Special Report on Global Warming of 1.5 °C from the Intergovernmental Panel on Climate Change (IPCC) highlighted the potential risks of climate change across different global warming levels (GWLs). The increasing occurrence of extreme high-temperature events is linked to a warmer climate that is particularly prevalent in the Arabian Peninsula (AP). This study investigates future changes in temperatures and related extremes over AP, under four GWLs, such as 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C, with three different Shared Socioeconomic Pathways (SSPs: SSP1-2.6, SSP2-4.5, and SSP5-8.5). The study uses high-resolution datasets of 27 models from the NASA Earth Exchange Global Daily Downscaled Projections of the Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6). The results showed that the NEX-GDDP-CMIP6 individual models and their multi-model means reasonably captured the extreme temperature events. The summer maximum and winter minimum temperatures are projected to increase by 0.11–0.67 °C and 0.09–0.70 °C per decade under the selected SSPs. Likewise, the projected temperature extremes exhibit significant warming with varying degrees across the GWLs under the selected SSPs. The warm temperature extremes are projected to increase, while the cold extremes are projected to decrease under all GWLs and the selected SSPs. Overall, the findings provide a comprehensive assessment of temperature changes over AP in response to global warming, which can be helpful in the development of climate adaptation and mitigation strategies.