The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset is a new quasi-global, high-resolution (0.05°), daily, pentadal, and monthly precipitation dataset designed to support early warning systems for agricultural drought. CHIRPS combines satellite-based Cold Cloud Duration (CCD) observations with station data to produce precipitation estimates with low latency and high resolution. The algorithm uses a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, blends station data in two phases to produce preliminary and final products, and employs a novel blending procedure to assign interpolation weights based on the spatial correlation structure of CCD estimates. Validation results show that CHIRPS performs well in data-sparse regions and exhibits lower biases compared to other precipitation products. The dataset is particularly useful for hydrologic modeling and early warning of agricultural drought, as demonstrated in a case study of southeastern Ethiopia, where CHIRPS was used to drive the Variable Infiltration Capacity model to analyze recent changes in soil moisture, evapotranspiration, rainfall, and air temperatures. The analysis highlights the importance of near real-time precipitation data in predicting droughts and understanding the impacts of climate change.The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset is a new quasi-global, high-resolution (0.05°), daily, pentadal, and monthly precipitation dataset designed to support early warning systems for agricultural drought. CHIRPS combines satellite-based Cold Cloud Duration (CCD) observations with station data to produce precipitation estimates with low latency and high resolution. The algorithm uses a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, blends station data in two phases to produce preliminary and final products, and employs a novel blending procedure to assign interpolation weights based on the spatial correlation structure of CCD estimates. Validation results show that CHIRPS performs well in data-sparse regions and exhibits lower biases compared to other precipitation products. The dataset is particularly useful for hydrologic modeling and early warning of agricultural drought, as demonstrated in a case study of southeastern Ethiopia, where CHIRPS was used to drive the Variable Infiltration Capacity model to analyze recent changes in soil moisture, evapotranspiration, rainfall, and air temperatures. The analysis highlights the importance of near real-time precipitation data in predicting droughts and understanding the impacts of climate change.