Multi-Criteria Performance Evaluation of Gridded Precipitation and Temperature Products in Data-Sparse Regions

Inadequate climate data stations often make hydrological modelling a rather challenging task in data-sparse regions. Gridded can be used as an alternative; however, their accuracy replicating the climatology of region interest with low levels uncertainty is important to water resource planning. This study utilised several performance metrics and multi-criteria decision making assess widely gridded precipitation temperature against quality-controlled observed station records Lake Chad basin. The study’s findings reveal that products differ quality across selected metrics, although they are especially promising regards temperature. However, there some inherent weaknesses data. Princeton University Global Meteorological Forcing showed worst performance, Kling–Gupta efficiency 0.13–0.50, mean modified index agreement 0.68, similarity coefficient SU = 0.365, relative other satisfactory all stations. There were varying degrees mismatch unidirectional trends, hydro-climatic information level uncertainty. Assessment based on revealed Climate Research Unit, Precipitation Climatology Centre, Prediction Centre Unit exhibit better terms similarity, recommended for application impact studies—especially quantification projected hazards vulnerabilities policy