Performance evaluation of gridded satellite-based rainfall products against gauge observations in the Shire River Basin, Malawi

Shire River Basin (SRB), located in Malawi, Southern Africa. In this study, the performance of Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and the African Rainfall Climatology version 2 (ARC2) was evaluated to investigate their ability to replicate 12 rain gauge datasets in the SRB from 1983–2015. Validation was conducted at daily, monthly, and annual timescales using continuous and categorical statistical methods to evaluate the accuracy and applicability of these SRPs in the SRB. CHIRPS demonstrated consistent accuracy across most timescales, particularly during the wet season, while TAMSAT excelled in rainfall detection, supporting flood forecasting and timely decision-making in agricultural planning. PERSIANN-CDR consistently overestimated rainfall, limiting its use for drought monitoring, while ARC2 underperformed in most metrics. Spatial analysis revealed a north-to-south rainfall gradient, influenced by elevation and proximity to Lake Malawi. These findings highlight the potential of SRPs such as CHIRPS and TAMSAT as viable alternatives for hydrological applications in data-scarce regions like the SRB, emphasizing the need for thorough validation before use. • Validated CHIRPS, TAMSAT, ARC2, and PERSIANN-CDR against gauge rainfall data in the Shire River Basin, Malawi. • Satellite rainfall products showed better agreement with observed data at monthly and annual timescales. • CHIRPS estimated rainfall amounts well; TAMSAT excelled in detection and showed balanced performance across metrics. • CHIRPS and TAMSAT captured spatial rainfall patterns linked to elevation and proximity to Lake Malawi. • Strategic selection of SRPs for hydrological applications in data-scarce regions.