<![CDATA[Global Precipitation Measurement (GPM) – Integrated Multi-satellite Retrievals for GPM (IMERG) satellite data is one of the main sources of global rainfall estimates with high spatial and temporal resolution. This study aims to identify spatial, temporal, and methodological trends in research related to the use of GPM-IMERG between 2014 and 2024, as well as to assess the effectiveness of various bias correction methods in improving data accuracy and to examine the main hydrological applications utilising these data. A systematic review of over twenty scientific publications reveals that the use of GPM-IMERG has grown considerably since 2017, particularly in humid tropical regions such as Southeast Asia, South Asia, and Latin America. Methodologically, there has been a shift towards integrating GPM-IMERG with physically based hydrological models (e.g. SWAT, HEC-HMS and VIC) and machine learning algorithms (e.g. Random Forest and XGBoost) to improve the prediction of rainfall and river discharge. Analysis also shows that Quantile Mapping (QM) and Distribution Mapping (DM) provide the best correction performance, increasing NSE values by 20–35% in mountainous areas. Linear Scaling (LS) remains effective in tropical lowlands. The most prevalent hydrological applications are discharge modelling, flood analysis and drought monitoring. Key research gaps include the absence of long-term studies (>10 years), limitations in topographically complex regions and the lack of multi-sensor integration.]]>
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