RainMerge: An uncertainty-aware global framework for merging gauge, satellite, and reanalysis sub-daily precipitation

Reliable precipitation information underpins flood risk management, water resources planning, and climate risk assessment, yet remains highly uncertain. Precipitation information is derived from gauges and satellite or reanalysis products, each with complementary strengths. Gauge-independent merging provides a rigorous theoretical framework for combining multiple gridded products but cannot directly incorporate point gauges because of scale mismatches. Consequently, no unified framework exists that (i) integrates multiple satellite and reanalysis datasets in ungauged regions, (ii) incorporates gauge information respecting spatial scale differences, and (iii) delivers user-oriented outputs at a catchment scale without requiring detailed expertise in merging methodologies.

Here we introduce the RainMerge framework, which advances multi-source precipitation merging through three conceptual advances. First, RainMerge adopts a two-stage strategy in which rain occurrence is merged as a binary rain or no-rain product and subsequently used to condition the merging of precipitation magnitudes. Second, where ground observations exist, rain gauge uncertainty is quantified and spatially transported to nearby gauge-sparse and ungauged regions using geostatistical interpolation, enabling uncertainty-informed merging across all grid cells. Third, RainMerge is implemented as an automated framework that minimises subjective methodological decision making, allowing users to define catchment boundaries and obtain a state-of-the-art spatially distributed sub-daily precipitation product.

The framework is evaluated using multiple precipitation datasets and hydrological simulations. Results show improved precipitation realism, and more consistent hydrological simulations across contrasting gauge-density regimes. By integrating merging theories, uncertainty estimation, and reproducible practice, RainMerge supports transparent and transferable precipitation information for hydrological modelling, climate analysis, and decision making especially for developing countries, which are impacted by data-sacrcity.

RainMerge can be accessed from https://www.rainmerge.tech.