Droughts often evolve gradually and cover large areas, and therefore, affect many people and activities. This motivates developing techniques to integrate different satellite observations, to cover large areas, and understand spatial and temporal variability of droughts. In this study, we apply probabilistic techniques to generate satellite derived meteorological, hydrological, and hydro-meteorological drought indices for the world's 156 major river basins covering 2003–2016. The data includes Terrestrial Water Storage (TWS) estimates from the Gravity Recovery And Climate Experiment (GRACE) mission, along with soil moisture, precipitation, and evapotranspiration reanalysis. Different drought characteristics of trends, occurrences, areal-extent, and frequencies corresponding to 3-, 6-, 12-, and 24-month timescales are extracted from these indices. Drought evolution within selected basins of Africa, America, and Asia is interpreted. Canonical Correlation Analysis (CCA) is then applied to find the relationship between global hydro-meteorological droughts and satellite derived Sea Surface Temperature (SST) changes. This relationship is then used to extract regions, where droughts and teleconnections are strongly interrelated. Our numerical results indicate that the 3- to 6-month hydrological droughts occur more frequently than the other timescales. Longer memory of water storage changes (than water fluxes) has found to be the reason of detecting extended hydrological droughts in regions such as the Middle East and Northern Africa. Through CCA, we show that the El Niño Southern Oscillation (ENSO) has major impact on the magnitude and evolution of hydrological droughts in regions such as the northern parts of Asia and most parts of the Australian continent between 2006 and 2011, as well as droughts in the Amazon basin, South Asia, and North Africa between 2010 and 2012. The Indian ocean Dipole (IOD) and North Atlantic Oscillation (NAO) are found to have regional influence on the evolution of hydrological droughts.