The projection and identification of historical and future changes in climatic systems is crucial. This study aims to assess the performance of CMIP6 climate models and projections of precipitation and temperature variables over the Upper Blue Nile Basin (UBNB), Northwestern Ethiopia. The bias in the CMIP6 model data was adjusted using data from meteorological stations. Additionally, this study uses daily CMIP6 precipitation and temperature data under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios for the near (2015–2044), mid (2045–2074), and far (2075–2100) periods. Power transformation and distribution mapping bias correction techniques were used to adjust biases in precipitation and temperature data from seven CMIP6 models. To validate the model data against observed data, statistical evaluation techniques were employed. Mann–Kendall (MK) and Sen’s slope estimator were also performed to identify trends and magnitudes of variations in rainfall and temperature, respectively. The performance evaluation revealed that the INM-CM5-0 and INM-CM4-8 models performed best for precipitation and temperature, respectively. The precipitation projections in all agro-climatic zones under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show a significant (p < 0.01) positive trend. The mean annual maximum temperature over UBNB is estimated to increase by 1.8 °C, 2.1 °C, and 2.8 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5 between 2015 and 2100, respectively. Similarly, the mean annually minimum temperature is estimated to increase by 1.5 °C, 2.1 °C, and 3.1 °C under SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. These significant changes in climate variables are anticipated to alter the incidence and severity of extremes. Hence, communities should adopt various adaptation practices to mitigate the effects of rising temperatures.

Climate variability has adversely affected the agricultural production of smallholder farmers in developing countries like Ethiopia. This study aims to examine the overall vulnerability of smallholder farmers to climate variability in different agro-ecological zones of Legambo district, north central, Ethiopia. The research used quantitative and qualitative data collection methods through cross-sectional survey data, focus group discussions, and key informant interviews from 347 randomly selected smallholder farmers. Then, 48 sub-component indicators categorized into twelve major components and then into three contributing factors of vulnerability (exposure, adaptive capacity, and sensitivity) were used to assess the livelihood vulnerability index (LVI) and LVI-based on the Inter-governmental Panel on Climate Change (IPCC) approach method in three agro-ecological-zones for vulnerability analysis. LVI and LVI-IPCC results showed that cold highland agro-ecology was the most vulnerable, and midland agro-ecology was the least vulnerable to climate variability effects. These findings can guide policymakers in designing adaptive strategies to enhance the resilience of smallholder farmers to climate variability.