The study evaluates the drought characteristics in India over projected climatic scenarios in different time frames, that is, near‐future (2010–2039), mid‐future (2040–2069), and far‐future (2070–2099) in comparison with reference period (1976–2005). Standardized Precipitation Evapotranspiration Index (SPEI), a multi‐scalar drought index was used owing to its robustness in capturing drought conditions while accounting the temperature. Gridded rainfall and temperature data provided by India Meteorological Department (IMD) was used to perform bias correction of nine Global Climate Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) project. Quantile mapping was used to correct the daily rainfall data at seasonal scale whereas daily temperature data was corrected at monthly scale. Multi‐Model Ensemble (MME) was prepared for different homogeneous monsoon regions of India, namely Hilly Regions (HR), Central Northeast (CNE), Northeast (NE), Northwest (NW), West Central (WC), and Peninsula (PS). Taylor diagram statistics were used for the preparation of MME. The regional climate cycle obtained from MME was found to be in good agreement with observed cycle derived from IMD data. The Mann–Kendal trend test was employed to detect the trend in drought severity and magnitude whereas L‐moments based frequency analysis was used to assess the magnitude of extreme drought severity under different time frames. The study reveals an increasing trend in drought severity, duration, occurrences, and the average length of drought under warming climate scenarios. Furthermore, the area under “above moderate drought” (i.e., severe and extreme drought combined) condition was also found to be increasing in projected climate.

The last couple of decades have seen the outburst of a number of satellite-based precipitation products with Tropical Rainfall Measuring Mission (TRMM) as the most widely used for hydrologic applications. Transition of TRMM into the Global Precipitation Measurement (GPM) promises enhanced spatio-temporal resolution along with upgrades to sensors and rainfall estimation techniques. The dependence of systematic error components in rainfall estimates of the Integrated Multi-satellitE Retrievals for GPM (IMERG), and their variation with climatology and topography, was evaluated over 86 basins in India for year 2014 and compared with the corresponding (2014) and retrospective (1998–2013) TRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a majority of Indian basins, with significant improvement in low rainfall estimates showing smaller negative biases in 75 out of 86 basins. Low rainfall estimates in TRMM showed a systematic dependence on basin climatology, with significant overprediction in semi-arid basins, which gradually improved in the higher rainfall basins. Medium and high rainfall estimates of TRMM exhibited a strong dependence on basin topography, with declining skill in higher elevation basins. The systematic dependence of error components on basin climatology and topography was reduced in IMERG, especially in terms of topography. Rainfall-runoff modeling using the Variable Infiltration Capacity (VIC) model over two flood-prone basins (Mahanadi and Wainganga) revealed that improvement in rainfall estimates in IMERG did not translate into improvement in runoff simulations. More studies are required over basins in different hydroclimatic zones to evaluate the hydrologic significance of IMERG.

Traditional drought monitoring is based on observed data from both meteorological and hydrological stations. Due to the scarcity of station observation data, it is difficult to obtain accurate drought distribution characteristics, and also tedious to replicate the large-scale information of drought. Thus, Gravity Recovery and Climate Experiment (GRACE) data are utilized in monitoring and characterizing regional droughts where ground station data is limited. In this study, we analyzed and assessed the drought characteristics utilizing the GRACE Groundwater Drought Index (GGDI) over four major river basins in India during the period of 2003–2016. The spatial distribution, temporal evolution of drought, and trend characteristics were analyzed using GGDI. Then, the relationship between GGDI and climate factors were evaluated by the method of wavelet coherence. The results indicate the following points: GRACE’s quantitative results were consistent and robust for drought assessment; out of the four basins, severe drought was noticed in the Cauvery river basin between 2012 and 2015, with severity of −27 and duration of 42 months; other than Godavari river basin, the remaining three basins displayed significant negative trends at monthly and seasonal scales; the wavelet coherence method revealed that climate factors had a substantial effect on GGDI, and the impact of Southern Oscillation Index (SOI) on drought was significantly high, followed by Sea Surface Temperature (SST) Index (namely, NINO3.4) and Multivariate El Niño–Southern Oscillation Index (MEI) in all the basins. This study provides reliable and robust quantitative result of GRACE water storage variations that shares new insights for further drought investigation.