Understanding the spatiotemporal patterns of drought is crucial for planning, disaster preparedness, vulnerability assessment, impact evaluation, and policy formulation to mitigate drought-induced effects. The purpose of this study was to assess rainfall trends and spatiotemporal patterns of meteorological drought using geospatial techniques in Menna watershed. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall, and station-based observed rainfall were the datasets used. The station-based rainfall was used to confirm the accuracy of CHIRPS rainfall data. The Mann-Kendall (MK) test and Sen's slope estimator were utilized to assess trends and ascertain the extent of change. To characterize meteorological droughts, percent of normal (PN), standardized anomaly index (SAI), and standardized precipitation index (SPI) were computed during the crop growing seasons (2000–2022). The validation result confirmed a strong agreement between the observed and CHIRPS rainfall data (R2 = 0.88). Based on the MK test, an increasing trend has been observed in annual (3.7 mm/year) and belg (3.4 mm/year) rainfall, which was significant at p < 0.05. But the kiremt season was slightly decreasing (−0.7 mm/year). The PN, SAI, and SPI values detected that 2002, 2004, 2009, 2011, 2014, 2015, and 2019 were drought years in the area. Even only 1.4, 0.2, and 0.5% of the watershed were free from drought in 2009, 2014, and 2015, respectively, due to extremely high rainfall deficiency. Conversely, 2001, 2010, and 2016 were notable for having the highest amounts of rainfall compared to the other years. Generally, the region could be classified as an area highly susceptible to meteorological drought in northwestern Ethiopia. There was no even a single year free from drought in the entire study period. To that extent, about 86% of it had repeatedly encountered extreme rainfall deficit (7–23 times) during the study period. Thus, the population has always been repeatedly smashed down by the frequent droughts. To tackle existing challenges and mitigate upcoming risks, continual droughts monitoring and implementation of efficient early warning systems are vital for the region.

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.

Climate change poses a significant threat to human society and natural ecosystems. Therefore, assessing the historical and future climate variables is crucial for implementing effective adaptation and mitigation strategies. Hence, this study evaluates the performance of CMIP6 climate models and projects future climate variables over the Amhara National Regional State, Ethiopia. The study was used observed data for model evaluation and bias correction. Fifteen CMIP6 climate models were used to project future climate variables under medium (SSP2-4.5) and high (SSP5-8.5) emission scenarios. The study also used Mann–Kendall and Sen’s slope tests were employed to detect trends and magnitude of change. The finding of the study revealed that model EC-Earth3-CC was the most reliable model for precipitation with R² = 0.86 and r = 0.93, while model AWI-ESM-1–1-LR performed best for temperature with R² ≈ 0.77–0.63 and r ≈ 0.88–0.81. The result also showed an increase in rainfall amount by about 82.3 mm (7.3%) under SSP2-4.5 scenario and 199.7 mm (17.6%) under SSP5-8.5 from the period 2015–2100. In addition, mean annual maximum temperature over the study area is projected to increase by 0.7 °C and 0.9 °C under SSP2-4.5 and SSP5-8.5, respectively. Similarly, the mean annual minimum temperature is estimated to increase by 0.7 °C and 1.0 °C under SSP2-4.5 and SSP5-8.5, respectively. During the period between 2075–2100, temperature increases relative to the baseline period (1990–2014) could reach 1.0 °C and 0.9 °C (SSP2-4.5) and 1.4 °C and 1.6 °C (SSP5-8.5), respectively. The spatial analysis shows maximum and minimum temperature were observed in northwestern and southern part of the study area, respectively. Hence, the finding of this study is vital for developing evidence-based and targeted climate adaptation strategies and highlight the need for improving climate models to better assess future climate change impacts and understanding of regional climate variability and extremes in the region.