The central highlands of Ethiopia’s Abbay Basin, a region highly dependent on rain-fed agriculture, face significant challenges due to climate change and variability, threatening agricultural productivity. This dissertation address the critical need for reliable rainfall data, and understanding of rainfall variability and trends, projection future climate scenarios, and a clear assessment of the vulnerability and the dynamics of adaptation strategies implemented by smallholder farmers. The study seeks to answer: which satellite-based precipitation dataset is most reliable for the study area? How variable are rainfall patterns, and what are the trends? What are the likely future changes in rainfall and temperature? How vulnerable are local communities, and How adaptation strategies variable from time to time and place to place? The study area covers the west Gojjam Zone in the central highlands of Abbay Basin. Using four satellite precipitation data estimators (SPEs), Climate Hazards Group Infrared Precipitation Stations (CHIRPSv2), the Climate Prediction Center (CPC) morphing technique (CMORPH), the Integrated Multi-satellite Retrieval for GPM (IMERG-06) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSSIANN-CCS) and GCM/CMIP6 precipitation and temperature (max and min) data, against ground-based gauged data and blended/ENACTS data, respectively. Household survey and KI also included. The research applied categorical (Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and Accuracy) and continuous (Root mean square error (RMSE), Relative bias, bias ratio) metrics for performance evaluation. Rainfall variability was analysed, parameters on onset and cessation dates, dry spells, Coefficient of Variation (CV %), and Standardized Rainfall Anomalies (SRA) were utilized to evaluate rainfall variability and seasonality. Trend analysis was carried out using Mann-Kendall test and Sen’s slope estimator. Climate projection relied on CMIP6 models under Shared Socioeconomic pathways (SSP2-4.5 and SSPS5-8.5) scenarios. Bias corrected data was extracted with Climate Model for hydrology (CMhyd) tool while vulnerability was assessed via Livelihood Vulnerability Index (LVI) and LVI-IPCC frame work. Descriptive statistics and Chi-square test was included to investigate the variation of adaptation strategies between the study agro ecological zones and decadal variation. IMERGE-06 emerged as the most reliable rainfall dataset, with CMORPH excelling in event detection (POD 0.9, CSI 0.74). The region showed moderate rainfall variability (CV: annual 10.7%, kiremt 11.7%, belg 10.6, and bega 22.6%), with delayed onsets (mean 142) Day of the Year (DOY) and early cessation date (279 DOY) shortening growing season. Trend analysis revealed significant annual (+9.14 mm/year) and belg (+6.94 mm/year) rainfall increases, while kiremt rainfall decline slightly. Future projection indicate annual rainfall increase up to 32% by mid-century and temperature rises up to 29oC under a high emission scenario. Vulnerability analysis found highland communities more exposed and less adaptive, with midland projected to be more vulnerable. Furthermore, the study area demonstrated significant variations in adaptation strategies within the Dega and W/Dega AEZs. During the 1990s, adaptation strategies focused on farmland expansion and intensification of irrigation. In the 2010s, farmers increasingly adopted row planting, greater use of fertilizer, crop diversification and improved seed varieties. By the 2020s, strategies shifted towards adjusting cropping calendars, further diversifying crops, and increasing the use of fertilizer and pesticides. In conclusion, context specific adaptation strategies, improved climate information, and institutional support are recommended to enhance resilience. Future research should incorporate additional climate variables and expand vulnerability assessment to better inform sustainable adaptation planning.