Validation of gridded precipitation products (GPP's) increases the confidence of the users and highlights possible improvements in the algorithms to handle complex rain forming processes. We evaluated the skill of three GPP's (CHIRPS-v2, CHELSA, and TerraClimate) in estimating the gauge observations and compared the precipitation trends derived from these products across the East and Southern Africa (ESA) region. We used Taylor diagrams and Kling-Gupta Efficiency (KGE) to assess the accuracy. A modified Mann-Kendal test and the Sen' slope estimator were utilized to determine the trends' significance and magnitude, respectively. The three GPP's had varied performance over temporal and altitudinal ranges. The skill of the three GPP's at monthly scale, was generally high but showed lower performance at elevations over 1500 m, especially during the OND season. The three GPP's performed equally well between the 1001 – 1500 m elevation range. CHELSA-v2.1 was most accurate at 0-500m but had the lowest skill at 501 – 1000 m and above 1500 m elevations that caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and large inland water bodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validating climate datasets to avoid error propagation in different models and applications. Our results demonstrate that new or higher-resolution precipitation data are not always accurate since an algorithm update can introduce artifacts or biases.

https://doi.org/10.20937/atm.53177

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https://doi.org/10.20937/atm.53177

Biometrics

Biometrics; Climate Change

Climate Change; Data; Analysis; Trends

Africa; East Africa

Tanzania

Eastern Africa Hub

Atmosfera