Spatiotemporal Evaluation of Multi-Source Precipitation Products in the Sudan Sahel: Evidence from White Nile State

Accurate rainfall estimates are essential for managing water resources and planning for climate risks in semi‑arid regions, yet long‑term gauge networks in these environments are often extremely limited. In this study, we evaluate three widely used multi‑source precipitation datasets; CHIRPS, IMERG, and ERA5‑Land, against long‑term observations from Ed Dueim and Kosti, the two main reference stations in White Nile State, central Sudan. The assessment covers monthly and annual scales across each product’s available record (1952–2022) and uses a broad set of metrics, including Pearson and Spearman correlations, NSE, KGE, RMSE, MAE, percent bias, and categorical detection scores (POD, FAR, CSI). All three datasets capture the region’s single‑peak June–October monsoon pattern, but their accuracy differs sharply when it comes to rainfall amounts and year‑to‑year variability. CHIRPS performs best overall, with monthly NSE values around 0.77 and KGE between 0.79 and 0.88, along with a consistent dry bias of 5–13%—a predictable error that can be corrected operationally. IMERG shows strong monthly correlations but consistently overestimates rainfall by 25–42%, which leads to unreliable annual totals (NSE = −1.93 to −2.21). ERA5‑Land performs worst across nearly all metrics, with monthly NSE near or below zero, annual NSE dropping to −15.34, and frequent false alarms during the dry season. Taken together, the evidence points to CHIRPS as the most reliable dataset for routine hydro‑climatic monitoring in White Nile State, while IMERG and ERA5‑Land may still be useful in more specialized or time‑specific applications.