| Summary: | Study region: This study focuses on Tekeze river basin of northern Ethiopia, and it is characterized by a typical dry biogeophysical environment. Study focus: In recent years, recurrent droughts are having an adverse impact on agricultural production and water resources in northern Ethiopia. Climate change through changes on temperature, precipitation and streamflow, may further strain this critical situation. This study has investigated the observed (1961–2014) and potential (2006–2099) hydro-climatic changes in Tekeze river basin of northern Ethiopia. Artificial Neural Networks (ANNs) are used to downscale temperature and precipitation predicated by 30 General Circulation Models (GCMs) as well as the projected streamflow changes for two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenario. New hydrological insights for the region: Results indicate that the variability of climatic factors as temperature and precipitation was observed to be both spatially and temporally diverse for the considered Tekeze river basin. Accordingly, the response of streamflow was also spatiotemporally complex. GCMs were evaluated with several performance indictors regarding patterns in hydro-climatic variables. The analysis showed the superiority of the multimodel ensemble means compared with individual GCM output. GCM projections for the 21century indicate a gradual reductions in streamflow attributed to the combined effect of increasing temperature and decreasing precipitation. The persistent increase of temperature and decrease of precipitation will have negative impacts on water availability and agriculture, hence site specific adaptation strategies are necessary. Keywords: Climate change, Temperature, Precipitation, Streamflow, GCM, Artificial neural networks
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