Drought is one of the most destructive natural hazards, affecting millions of people worldwide. Meteorological drought (MD), caused by a deficit in precipitation, can trigger hydrological drought (HD). However, little is known about the triggering MD events in space and time that propagate to HD under climate change. In this study, a high-resolution gridded streamflow (0.1°) was simulated for the Gidabo catchment in Ethiopia. The gridded HBV hydrological model was employed for streamflow simulation using inputs of precipitation, potential evapotranspiration, and HBV parameters (all gridded at 0.1°), for both historical and future periods. Consequently, the gridded meteorological and hydrological droughts were quantified using standardized indices. Drought propagation and its characteristics were analyzed, by examining changes from the historical to future periods. Additionally, the drought propagation ratio (PR) was quantified by systematically identifying the triggering MD events. The results showed an efficient model performance from using gridded input forcing data and global parameters, achieving median KGE values of 0.55 and 0.77 during monthly calibration and validation respectively. The highest Pearson correlation values were observed between SSI-1 and SPI-3. Spatially, the majority of grid cells in the northeastern part of the catchment exhibited the strongest propagation. Under climate change, the historical period propagation value of 0.83 decreased to 0.80 and 0.81 in the near future (NF) and far future (FF) periods, respectively. The higher PR were observed in the northeastern part of Gidabo catchment, while PR decreased in the central and southwestern regions, indicating greater resilience in these areas. Among drought characteristics, peak drought in most grid cells showed a percentage reduction in the NF and MF periods. This methodology advances the understanding of drought propagation dynamics and enables the prioritization of mitigation strategies at grid-scale under climate change.