Ethiopia is one of the most vulnerable countries to climate variability due to its dependency on rain-fed agriculture and low adaptive capacity. Suha watershed, the study area, is one of those affected areas in Northwest Ethiopia. Thus, the study aimed to assess the spatio-temporal variability of climate, its relation with LULC change, the vulnerability of smallholder farmers to climate variability, and the challenges in using adaptation measures in the watershed. To see this, climate data from the National Metrological Agency of Ethiopia and the Royal Dutch Meteorological Institute (KNMI), satellite imageries of Landsat, and socioeconomic data from sample households were used. The analysis methods such as the Mann– Kendall test, Sen’s slope estimator, coefficient of variation-all to see the trend/variability, ARIMA to pro ject, and the Inverse Distance Weighted tool to depict the spatiotemporal trend of rainfall and temperature were used. The relationship between NDVI and climate variables was determined using Pearson’s corre lation coefficient, while the cellular automata-artificial neural network (CA-ANN) technique was used to predict future LULC change. The vulnerability of climate variability was assessed using the LVI-IPPC composite index whereas the factors that affect the choice of smallholder to use adaptation measures were assessed using the binary logit model. The Land surface temperature (LST), LULC change, NDVI values, and rainfall trend were analyzed using ArcGIS 10.7.1, QGIS 2.8.3, R, Excel sheet, and XLSTAT 19 software. The coefficient of variation indicated a higher seasonal variability compared to year-to-year in the water shed. The Mann-Kendall test and Sen's slope estimator showed a non-significant decreasing trend for Kiremt season at p<0.05. The upper part of the watershed is wetter than the lower portion, in which 40% of it received 930-1024 mm/annum. The minimum temperature showed a decreasing trend, while the max imum temperature exhibited an increasing trend. Future projections suggested a decreasing trend in mean annual rainfall and an increasing trend in mean annual temperature. Concerning the LULC dynamic, the finding revealed that among the six land use/land cover classes, cultivated land gained more than 30 per cent, while grassland lost more than 20 percent in each decade. LST and NDVI values showed a negative correlation, but not with rainfall in the area. The NDVI value of the different land uses showed a decreasing trend. However, the LST showed an increasing trend, which is highly associated with the rapid conversion of current and future vegetated areas compare to the non-vegetated one. The local farmers’ vulnerability to climate variability assessment also showed a significant difference (as of -0.07, 0.03, and 0.18 for mid land, highland, and lowland AEZs, respectively) across the agro-ecological zones. The lowland AEZ was more vulnerable, while the midland AEZs had higher adaptive capacity and was less vulnerable. This was related to the differences in agro-ecological and socio-economic factors. The majority of the respondents were aware of the prevailing climate variability in their area and tried to use different adaptation measures. Nonetheless, except for location and livestock ownership, all the others influenced some strategies nega tively. Hence, due attention should be given to the crafting and application of area-specific adaptive strat egies to curb the undesired potential impact of climate variability in association with the LULC dynamics. However, the use of an efficient adaptation measure against climate variability needs to consider the influ ence of those factors based on agro ecological zone of the farmers.