A Modular and Transferable Framework for Enhancing Satellite-Derived Daily Precipitation: Adjusting Values, Aligning Distributions, and Preserving Extremes

Satellite-based precipitation products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG, V07) provide global coverage but exhibit systematic biases in daily accumulations, particularly for extreme events. This study presents a hybrid bias-correction framework (LSEQM+DL) for daily satellite precipitation that sequentially integrates Linear Scaling (LS) for mean bias, Empirical Quantile Mapping (EQM) with a Generalized Pareto Distribution (GPD) tail adjustment for distributional alignment, and a Convolutional Neural Network (CNN) refinement that targets extreme-precipitation pixels. A station-density confidence mask scales the deep-learning influence with gauge density, so the CNN refinement is strongest where the reference, the CPC Unified Gauge-Based Analysis of Daily Precipitation (CPC-UNI), is best constrained. The framework targets the IMERG Late Run (IMERG-L), whose roughly 14 h latency suits near-real-time flood monitoring. It is applied over Indonesia (2001–2025) and evaluated against CPC-UNI and 171 independent stations of the Meteorological, Climatological, and Geophysical Agency (BMKG) through three pillars: adjusting values, aligning distributions, and preserving extremes. At independent stations, the correction brings the standard deviation ratio from 0.71 (LS) to 1.00, the relative bias from −11.4% to −0.6%, and the 99th-percentile ratio from 0.71 to 1.01, and reduces a 21% over-estimation of wet-day frequency to within 5% of that observed. These gains carry a designed cost: the probability of detection falls from 0.78 to 0.65, while pixel-level temporal metrics (correlation, root-mean-square error, Nash–Sutcliffe efficiency) remain largely unchanged, confirming that the framework improves statistical properties rather than day-to-day timing. Relying only on globally available satellite and gauge-analysis data, and degrading gracefully where gauges are sparse, the framework is portable in principle with regional recalibration of its three tuning parameters. The corrected near-real-time product, with its station-density mask as a spatially explicit quality indicator, is intended to support flood monitoring, water resource management, and climate risk assessment in Indonesia and other gauge-sparse tropical regions.