Hydro-climatic extremes can affect the reliability of electricity supply, in par-ticular  in  countries  that  depend  greatly  on  hydropower  or  cooling  water  andhave a limited adaptive capacity.  Assessments of the vulnerability of the powersector and of the impact of extreme events are thus crucial for decision-makers,and yet often they are severely constrained by data scarcity.  Here,  we intro-duce and validate an energy-climate-water framework linking remotely-senseddata  from  multiple  satellite  missions  and  instruments  (TOPEX/POSEIDON.OSTM/Jason, VIIRS, MODIS, TMPA, AMSR-E) and field observations.  Theplatform exploits random forests regression algorithms to mitigate data scarcityand predict river discharge variability when ungauged.  The validated predic-tions are used to assess the impact of hydroclimatic extremes on hydropowerreliability  and  on  the  final  use  of  electricity  in  urban  areas  proxied  by  night-time light radiance variation.  We apply the framework to the case of Malawifor the periods 2000-2018 and 2012-2018 for hydrology and power, respectively.Our  results  highlight  the  significant  impact  of  hydro-climatic  variability  anddry extremes on both the supply of electricity and its final use.  We thus showthat a modelling framework based on open-access data from satellites, machinelearning algorithms, and regression analysis can mitigate data scarcity and im-prove the understanding of vulnerabilities.  The proposed approach can supportlong-term infrastructure development monitoring and identify vulnerable pop-ulations, in particular under a changing climate.