This research is part of the ongoing research project − Climate change Adaptation to ManagE the risks of extreme hydrologicaL and weather events for food security in vulnerable west Nile delta (CAMEL). The study area, West Nile Delta, is an important region in Egypt featuring agricultural and industrial significance to the nation, whilst it faces serious crises from the interaction of complex environmental problems (e.g. flooding) which is exacerbated by climate change in the recent decades. Under the pressure of growing population, food security has become a national issue. In the latest decades, the region has experienced more extreme weather events; the severe rainfall events have resulted in flooding destroying massive crops and causing losses of human life and livestock. The evolvement of society in this region has made the people living in the flood-prone area – mostly farm labours − relatively socio-economic vulnerable. This research hence focuses on the urgent foregoing issue − disastrous pluvial flooding, which seeks to mitigate the issue of crop production loss and human casualty caused by climate change. Therefore, an adaption measure of an early warning system for extreme events caused by heavy rainfall has become an urgent demand. However, the scarcity of data (e.g. insufficiency in the coverage of gauge stations and radar stations) has always been a main obstacle to relevant measures in Egypt. The research hence seeks to cope with such difficulty whilst to build an integrated flood early warning system for Egypt. Based on the integration of Nowcasting method (applying GPM and MPE satellite radar observation) and NWP method (downscaling ECMWF data) as the substitution for the insufficient ground observations, the integrated approach can take the advantages of both data sources to perform better forecasting. However, GPM and MPE data, compared with ground observation data, still reflects relative disadvantages in spatial and temporal resolution in terms of Nowcasting application. Besides, notwithstanding Nowcasting method can make up for the spatial resolution of the NWP method, its mainstream − optical flow approach based on the Lagrangian method − still lacks confidence in dealing with local advection circumstance, as well as fast and drastic formation and dissipation of precipitation. The research hence seeks to improve Nowcasting, by applying a phase-based frame interpolation method based on the Eulerian method, to refine the resolution of data to improve the performance of Nowcasting. It features better performance in precipitation change, strong precipitation divergence (i.e. light contrast), and computational efficiency. The improved Nowcasting, for further integrating with the NWP method, is being tested and proposed, which will end up with a recommendation of policy and a novel tool – real-time flood early warning system – so as to accommodate the hydrological extremes towards climate change in Egypt.