Wildfires and their associated impacts have increased significantly over the past two decades, heightening the risk to human life and causing substantial damage to forest ecosystems in the Amazon and Andean regions. To mitigate these impacts, the assessment of both seasonal and intra-seasonal wildfire hazard is of critical importance. The availability of satellite data, together with the implementation of regional wildfire early warning systems based on hazard indices, represents a key component in reducing wildfire risk. This study aims to develop a new short-term wildfire hazard model based on both observational and satellite-derived datasets, including data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Global Precipitation Measurement (GPM). The proposed Wildfire Hazard Index (WHI) is derived from Dry-Day Frequency (DDF), the Normalized Difference Vegetation Index (NDVI), cultivated area (CA), and forest fuel (FF). A wildfire hazard classification scheme (extreme, very high, high, moderate, and low) is introduced to generate hazard maps. The results indicate that DDF, NDVI, CA, and FF are key variables for analyzing the spatial and temporal variability of wildfire hazard. Lower WHI values correspond to lower wildfire hazard, and vice versa. The findings suggest that the model adequately represents seasonal wildfire hazard, demonstrating consistency among WHI values, wildfire occurrence, and MODIS-derived hotspots. Overall, this study shows that the proposed method can support hazard classification and contribute to the implementation of regional wildfire early warning systems based on precipitation forecasts in the Amazon and Andean regions.