Understanding trends in crop phenological variables and the climate drivers is indispensable for devising precision agricultural system. Studies conducted using remote sensing technologies at the national scale in Ethiopia have focused on the relationships of climate variability with vegetation greenness. However, assessment of crop phenological variable trends and the climate drivers were unexplored. Therefore, the study aimed to understand the trends in crop phenological variables and the climate derivers. To this end, phenological variables extracted from Normalize Difference Vegetation Index (NDVI), Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) were used. Savitzky– Golay filter method employed to smooth NDVI time series data and relative threshold method applied to extract phenological variables such as start of season (SOS), length of grow the season (LOS) end of growing season (EOS) and peak greenness time (POS). Trend analysis of crop phenological variables, precipitation and land surface temperature examined by Mann Kendall test and slope of the trend calculated by Sen’s slope method. Linear regression model used to evaluate the influence of changes in climate variables on crop phenological variables. End of crop growing season showed strong decreasing trend in the past twenty years (p < 0.05). May precipitation amount showed substantial increasing trend (p = 0.013). Minimum land surface temperature in May portrayed positive statistically significant trend. Peak greenness time had showed significant correlations with May, July and October precipitations (P < 0.05). Precipitation amount in May and June had showed strong positive correlation with start of crop growing season (p < 0.001), whereas length of crop growing period showed positive significant correlation with precipitation amount in July and August (p < 0.001). On the other hand, maximum land surface temperatures in May and June had strong positive correlation with start of crop growing season. In conclusion, the trends of crop phenological variables are advanced and shorten. Hence, farmers should consider crop varieties or types that need short development period. The research results can be used as an input in adaptive crop management guideline preparations. Keywords: CHIRPS, MODIS, land surface temperature, phenology, precipitation