Investigation of the pressing impacts of climate change on drought is vital for sustainable societal and ecosystem functioning. The magnitude of how much the drought will change and the way how droughts would affect society and the environment are inadequately addressed over East Africa. This study aimed at assessing future drought changes using an ensemble of five Global Climate Models (GCMs) in the Coupled Model Intercomparison Project (CMIP5) over East Africa. To this end, drought characteristics were investigated under the Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 in the near‐term (the 2020s; 2011–2040), mid‐century (2050s; 2041–2070), and end‐of‐century (2080s; 2071–2100). The changes of the Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) were first compared, and the SPEI was used for measuring future droughts as it showed stronger changes due to its inclusion of temperature effects. Drought area in East Africa is likely to increase at the end of the 21^st century by 16%, 36% and 54% under RCP 2.6, 4.5 and 8.5, respectively, with the areas affected by extreme drought increasing more rapidly than severe and moderate droughts. Spatially, drought event, duration, frequency and intensity would increase in Sudan, Tanzania, Somalia, South Sudan, but generally decrease in Kenya, Uganda and Ethiopian highlands. Results also confirm that drought changes over East Africa follow the “dry gets drier and wet gets wetter” paradigm. The findings provide important guidance for improving identification of causes, minimizing the impacts and enhancing the resilience to droughts in East Africa.

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979–2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of −4.2 ± 2.9 mm yr−1 decade−1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979–2016.

Satellite rainfall products are an important source of rainfall data in un-gauged catchments. However, these products need to be validated as their accuracy can be affected by geographical position, topography, climate and embedded algorithms. Eight satellite rainfall products such as African Rainfall Climatology (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPs), Global Precipitation Climatology Project GPCP), CPC Morphic technique (CMorph), Atmospheric Administration Climate Prediction Center (NOAA-CPC) merged analysis (CMap), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation Algorithm version 2 (RFEv2) and Tropical Rainfall Measurement Mission (TRMM) were evaluated against ground observations over the complex topography of the upper Tekeze-Atbara basin in Ethiopia. The accuracy of the datasets was evaluated at different temporal and larger spatial scales over the period 2002–2015. The results show that the rainfall data of CHIRPS outperformed all other products at all temporal and spatial scales. Next to CHIRPS, estimates from RFEv2, 3B42v7, and PERSIANN products are closest to the measurements at rain gauges for all spatiotemporal scales: daily, monthly and seasonal, and both at point and spatial scales. The percentage bias (PBias) and correlation coefficient (r) of these products were within ±25% and >0.5 for all scales. The remaining products performed poorly with PBias up to 200% and lower r (<0.5) at all scales. However, the performance of all products improved as the temporal scale increased to month and season at all spatial scales. Compared to low altitudes 3000 m.a.s.l.) increased by 35% whilst r dropped by 28%. CHIRPS and 3B42v7 products showed best agreement in mountainous terrains. However, all datasets show no consistency of the error sign. CMorph and 3B42v7 consistently overestimate rainfall relative to all rain gauges during the pixel-to-point rainfall comparison approach and at lowland areas during the areal averaged rainfall comparison. The other six products showed a clear underestimation at all spatial scales. In summary, the results show that rainfall estimates by CHIRPS, RFEv2 and 3B42v7 have a consistently better agreement with ground rainfall at all spatiotemporal scales. Considering the complex topography and limited gauges, the performance of CHIRPS, RFEv2 and 3B42v7 indicates that these products can be used for hydrological and overall water management applications in the region.