Rainfall plays a critical role in shaping India’s water resources, agriculture, and economy, especially during the Indian Summer Monsoon, which brings about 75% of the country’s annual rainfall. To monitor and understand these patterns, scientists use various precipitation datasets derived from satellites, weather models (reanalysis), and ground-based rain gauges. However, it is often unclear how reliable these different datasets are, particularly across India’s diverse landscapes and climate zones. In this study, we evaluate four widely used precipitation products to determine how well they represent monsoon rainfall across India. We use a statistical technique called triple collocation, which estimates the accuracy and agreement of datasets without needing a perfect reference. This is the first study in India to apply this method to assess monsoon rainfall performance. We introduce a new scoring method, the Total Performance Score, to rank datasets based on accuracy and consistency. Our results show that some products work better in certain regions or climate types. For example, reanalysis datasets perform best in high-rainfall areas, while others are more reliable in transitional or drier regions. These findings can help scientists, policymakers and disaster managers choose the most suitable data for monitoring weather, managing water and preparing for climate extremes.