A reproducible geospatial benchmarking workflow for gridded precipitation products under dense-gauge co-availability

Benchmarking heterogeneous geospatial precipitation sources is difficult because product error, target support, gauge density, spatial alignment, and local alternatives are often conflated. Here we present an auditable geospatial benchmarking workflow for evaluating gridded precipitation products under dense-gauge co-availability. The workflow defines a common hourly cell--hour universe, harmonizes IMERG, ERA5, and EURADCLIM on the IMERG grid, constructs a within-cell median gauge target from complete AVAMET stations, and compares each gridded source with leave-cell local baselines that exclude gauges from the target cell. It further stratifies performance by rain activity, gauge support, and observable inter-product conflict, allowing gridded-source competitiveness to be assessed conditionally rather than through a single global ranking. In an eastern Spain demonstration testbed for 2023, the workflow shows that aggregate rankings mask regime-specific behavior: conflict separates low-disagreement screening conditions from rain-active or high-disagreement cross-checking conditions, low-conflict gridded gains are dominated by dry-hour composition, stronger local support hardens the benchmark, and source routing has diagnostic value but is sensitive to annual composition. The benchmark is conditional on dense local co-availability and should not be interpreted as standalone gridded-product validation in sparse-gauge regions. The resulting artefact is a reusable computational benchmark rather than a regional product ranking. Beyond the demonstration testbed, the contribution is a reusable benchmark data model and reference implementation that separates target construction, leakage-controlled local comparison, product disagreement, support stratification, and manuscript-level regeneration. The workflow and frozen artefacts provide a reusable template for auditable benchmarking of gridded environmental products in dense-observation settings.