Multi-criteria framework for evaluating gridded precipitation climate products in the Sahel region

Sahel region, West Africa. The region is among the most climate-exposed regions globally, marked by high rainfall variability, frequent droughts and floods, and limited availability of dependable ground-based observations. Despite growing availability of gridded climate products (GCPs), there is limited comprehensive and multi-criteria evaluations of their performances across the region. This study presents an integrated three-module evaluation framework that assesses GCPs through (i) statistical performance, (ii) extreme-event characterization, and (iii) drought representation. The framework combines Compromise Programming (CP), a Multi-Criteria Decision Making (MCDM) method, and Meta-Analysis (MA) to evaluate ten widely used GCPs comprising of gauge-derived products, satellite-derived, and reanalysis-based outputs using field observations. The results of the evaluation indicated that, the top three models in Module 1 are GPCC, CHIRPS 2.0, and GPCP. In Module 2, the top models are GPCC, CPC, and GPCP. In Module 3, GPCP, PERSIANN-CDR, and GPCC performed best. Reanalysis products underperformed in all of the modules. The overall Meta-Analysis ranking identified GPCC as the top-ranked product, followed by GPCP and CPC. Sensitivity analysis confirmed the robustness of the evaluation framework and the stability of GPCC under different weighting scenarios. This framework provides a reliable and replicable approach for evaluating precipitation datasets and supports the selection of skill GCPs for hydrological modeling, drought monitoring, and climate assessment across the Sahel. • A multi-criteria framework evaluated ten gridded precipitation products in the Sahel • Statistical, extreme-event, and drought modules captured key hydro-climatic behavior in the Sahel • GPCC consistently ranked highest across modules using CP, MCDM, and Meta-Analysis • Sensitivity testing confirmed the robustness and stability of the evaluation framework • Framework guides optimal dataset selection for drought, flood, and climate applications