Of long-term and future climate variability is crucial for impact assessment studies in drought-prone areas like the Giba basin in northern Ethiopia. This study has applied the statistical downscaling model (SDSM) and (De Martonne and Pinna combinative) aridity index methods to evaluate the climate system of the Giba basin. Historical data (1961–2019) from seven meteorological stations and global grided data were used for future climate projections (2020–2100) under the three emission scenarios (RCPs 2.6, 4.5, and 8.5) for the three-time horizons (2040s, 2060s, and 2080s). Analysis of results showed that rainfall and temperature projection on a monthly and/or seasonal basis has more significance than on an annual basis for impact studies particularly, in areas where irrigation practices are common like in the Giba basin. Seasonal projection of rainfall in the basin showed a slightly decreasing trend during the spring season (MAM), and a significant increment in the main rainy season (JJA) under all scenarios and for the whole projection year. On an annual basis, a maximum increase of rainfall, up to +285 mm/year and +298 mm/year was expected to increase at Abyi Adi and Mekelle Obs stations, respectively, under RCP 8.5 in the 2080s. Temperature projection showed a consistent rise throughout the basin that ranges from a minimum increase of Tmax by +0.29 °C in the 2040s (RCP 2.6) at Mekelle Obs station to a maximum increase of Tmin by +2.35 °C in the 2080s (RCP8.5) at Abyi Adi station. In general, it is observed that the rate of increment of projected Tmin was more than that of Tmax in all stations in the Giba basin, which showed a continuous contraction of the gap between Tmin and Tmax, hence, the prevalence of global warming. This has led to a considerable increment of aridity till the end of the 21st century. Hence, the implementation of locally-suited climate change resilient strategies is crucial to enhance the sustainability of the ecosystem and ensure food security in the basin.

Analysis of historical and future land use/land cover (LULC) dynamics using spatiotemporal data is crucial for better management of natural resources and environmental monitoring. This study investigated LULC transformations over a span of 60 years (1984–2044) for the Giba basin in northern Ethiopia. ArcGIS and the Cellular Automata and Artificial Neural Network (CA-ANN) model were used to develop the historical (1984, 2004, 2014, and 2024) and projected future (2034 and 2044) LULC maps of the basin, respectively. The results show that LULC categories experienced shifts from one class to another by 35%, 33%, and 40% in 2004–2014, 2014–2024, and 2004–2024, respectively. During 1984–2024, the largest and smallest percentage of positive changes were observed in settlement (7700%) and shrubs and bushes (25%), which increased from negligible to 78 km² and from 1668 km² to 2082 km², respectively. Furthermore, barren land and forestland showed the largest (−80%) and smallest (−37%) declines, which decreased from 956 km² to 187 km² and from 164 km² to 103 km² during the same period, respectively. Overall, the last 40 years witnessed considerable changes to LULC dynamics in the Giba basin. Cropland, water bodies, and settlements showed a continuously increasing trend throughout the historical study period, while grassland exhibited a continuous decreasing trend. Results of the CA-ANN model showed that the majority of the LULC categories (including water body, forest, bushes and shrubs, grassland, and barren land) will decrease, except for a slight increase of cropland (+6%) and settlements (+16%), which is projected to increase from 2570 km² to 2733 km² and from 78 km² to 91 km², respectively, in the next two decades, from 2024 to 2044. In general, high population increase, changes in government policies, and armed conflicts were found to be the most influential driving factors of LULC changes in the basin.