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Long‐term time series of climatological data measured at meteorological stations provide one of the most authentic annals of climate in the past. However, there are a host of factors that affect measurements of climate parameters and make the data unsuitable for direct use and analysis. Serial completion and homogenization thus have to be undertaken in order to draw valid conclusions about the climate, or to apply the time series in impact studies. This study reports on gap filling and homogenization results of climatological elements in the headwater region of the Upper Blue Nile Basin from 1980 to 2013. Firstly, approaches for reconstruction of the time series from neighboring stations using different techniques were compared and selected. Neighboring stations were selected based on horizontal distance and coefficient of correlation. Secondly, the reconstructed time series were homogenized using Multiple Analysis of Series for Homogenization (MASH). The results show improved spatial coherence of the final data series. Higher spatial coherence was revealed for maximum temperature than for minimum temperature and rainfall. For rainfall and minimum and maximum temperatures, the coefficient of correlation weighting method outperforms other candidate methods, such as the normal ratio method (NRM), the modified NRM, and the inverse distance weighting method. Rainfall series for half of the stations considered were found to be homogeneous, and thus inhomogeneity corrections were not applied. Inhomogeneity in the remaining stations either underestimates or overestimates annual rainfall series. All stations revealed inhomogeneity in the mean annual maximum and minimum temperature series. These serially complete and homogenized data on rainfall, and minimum and maximum temperatures of the present study can be used for climate change and hydrological studies in the basin.

The applicability of the regional climate model (RCMs) for catchment hydroclimate is obscured due to their systematic bias. As a result, bias correction has become an essential precondition for the study of climate change. This study aimed to evaluate the skill of seven rainfall and five maximum and minimum temperature RCM outputs against observed data in simulating the characteristics of climate at several locations over the Baro-Akobo basin in Ethiopia. The evaluation was performed based on raw and bias-corrected RCMs against observed for a long-term basis. Several statistical metrics were used to compare RCMs against observed using a pixel-to-point approach. In this finding, raw RCMs showed pronounced biases such as lower correlation and higher PBIAS in estimating rainfall and minimum temperature than maximum temperature. However, most RCMs after bias correction showed better performance in reproducing the magnitude and distribution of the mean monthly rainfall and temperature and improve all the statistical metrics. The Mann-Kendall trend test for observed and bias-corrected RCMs indicated a decreasing annual rainfall trend while the maximum and minimum temperature showed an increasing trend in most stations. In most statistical metrics, the ensemble mean resulted in better agreement with observation than individual models in most stations. In general, after bias correction, the ensemble adequately simulates the Baro-Akobo basin climate and can be used for evaluation of future climate projections in the region.