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The management of water resources is challenging under extreme climate conditions with the acceleration of climate change. Many existing indices for analysing extreme climate conditions focus on the statistical behaviour of a single hydrological variable, such as precipitation or temperature. Such indices have limitations in providing a comprehensive interpretations of elaborate extreme climate conditions. In this study, the hydrological severity index (HSI), which is defined as the ratio of precipitation to the sum of evapotranspiration, runoff, and terrestrial water storage from the water balance equation, was proposed and calculated using Global Land Data Assimilation System datasets for 1980-2010. Mean HSI was relatively high in tropical regions due to the high sensitivity of precipitation, while it was relatively low in semi-arid and arid regions due to low precipitation and high evaporative demand. HSI showed a good representation of hydrological severity based on the spatial pattern of the absolute correlation coefficient as well as the opposite pattern of the coefficient of variation calculated from HSI and the radiative index of dryness. Based on the aforementioned phenomenon, temporal anomaly HSI (HSIanom) was applied to Pakistan and Australia as case studies to analyse the 2010 Pakistan Flooding and the Australian Millennium Drought (especially in 2002 and 2006). The HSIanom was able to capture the severity of extreme climate events and to provide underlying causes accompanying the physical climate forcing system. Specifically, the spatial pattern of HSIanom was useful for analysing regions with high severity to extreme climate conditions. The HSI can be used to establish water management policies that consider regional hydrological features in order to cope with intensified climate change. In addition, stand-alone HSI could be used to predict future climate trends based on precise simulations of hydrological variables.