This study aims to investigate spatiotemporal variability, trends, and anomaly in rainfall and temperature in the Sidama region, Ethiopia. The TerraClimate gridded dataset on a monthly time scale for 30 years (1991–2020) with a horizontal resolution of approximately 4 km was used for the study. Trends in annual and seasonal rainfall and temperature were assessed using a nonparametric test (Mann-Kendal test) and Sen’s slope to test the statistical significance and magnitude of trends (increase/decrease), respectively. Our findings revealed that annual rainfall, summer (Hawado), and spring (Badhessa) rainfall have shown an increasing trend in most parts of the region, except for its northwest parts. We found a low annual rainfall variability (CV < 13%) over the southeastern and northwestern parts of the region. Rainfall variability revealed the difference in both time and space across the region. Six drought years (1999, 2001, 2002, 2003, 2012, and 2019) with different magnitudes were identified across the region. Annual average maximum (up to 0.4 °C decade−1) and minimum (up to 0.25 °C decade−1) temperatures revealed significantly increasing trends across the region. The standardized anomaly in the mean annual temperature indicated that the years in the recent decade (2011–2020) are getting warmer compared to the past two decades (1991–2010) due to natural and anthropogenic activities causing weather extremes in the region. The results of this study for rainfall contradict the other studies in the rift valley part of the region. Therefore, we suggest appropriate climate change adaptation strategies so that there is high rainfall and temperature variability across the region and between seasons.

This study aims to investigate spatiotemporal variability, trends, and anomaly in rainfall and temperature in the Sidama region, Ethiopia. The TerraClimate gridded dataset on a monthly time scale for 30 years (1991–2020) with a horizontal resolution of approximately 4 km was used for the study. Trends in annual and seasonal rainfall and temperature were assessed using a nonparametric test (Mann-Kendal test) and Sen’s slope, to test the statistical significance and magnitude of trends (increase/decrease), respectively. Our findings revealed that annual rainfall, summer (Hawado), and spring (Badhessa) rainfall have shown an increasing trend in most parts of the region, except for its northwest parts. We found a low annual rainfall variability (CV < 13%) over the southeastern and northwestern parts of the region. Rainfall variability revealed the difference in both time and space across the region. Six drought years (1999, 2001, 2002, 2003, 2012, and 2019) with different magnitudes were identified across the region. Annual average maximum (up to 0.4°C decade–1) and minimum (up to 0.25°C decade–1) temperatures revealed significantly increasing trends across the region. The standardized anomaly in the mean annual temperature indicated that the years in the recent decade (2011–2020) are getting warmer compared to the past two decades (1991–2010) due to climate change and other local and regional factors that cause weather extremes in the region. The results of this study for rainfall contradict the other studies in the rift valley part of the region. Therefore, we suggest the design and implementation of locally driven climate change adaptation strategies so that there is high rainfall and temperature variability across the region and between seasons.

The new SEAS5 global ensemble forecast system was dynamically downscaled over the Horn of Africa for summer (June-July-August) 2018. For this purpose, a multi-physics ensemble was designed with a grid increment of 3 km and without any intermediate nest based on the Weather Research and Forecasting model (WRF). The WRF and the SEAS5 model output were compared with each other and reference datasets to assess the biases in 4 different regions of Ethiopia. Also, the WRF ensemble variability was investigated in relation to model parameterization and lateral boundary conditions. Over the summer, the SEAS5 has a positive temperature bias of 0.17 C compared to ECMWF analysis average for the study domain, while the WRF bias is +1.14 C. Concerning precipitation, the WRF model had average accumulated values of 264 mm, compared to 248 mm for SEAS5 and 236 mm for the observations. Over south Ethiopia, however, the downscaling produced over 50% more precipitation than the other datasets. The maximum northward extension of the tropical rain belt was reduced by about 2 in both models when compared to observations. Downscaling increased reliability for precipitation, correcting the SEAS5 underdispersion: ensemble spread for precipitation was increased by about 70% in the WRF ensemble in three of the four Ethiopian sub-regions, whereas the very dry Somali region remained unaffected. The WRF ensemble analysis revealed that the ensemble spread is mainly caused by the perturbed boundary conditions, as their effect is often 50% larger than the physicsinduced variability in the mountainous part of Ethiopia for precipitation and temperature.

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&lt;p&gt;&lt;strong&gt;Abstract&lt;/strong&gt;. Climate regionalization is crucial for climate studies, especially in the case of heterogeneous regions like East Africa. This paper focuses on categorizing Ethiopia into homogeneous climatic sub-regions by applying a classi&amp;#64257;cation of circulation patterns on precipitation. The sub-regions obtained will be applied on the verification of WRF-NOAHMP seasonal simulations performed over the Horn of Africa. We analyzed the occurrence of each circulation type per month and per year over the whole country. Then, trend analysis of temperature and precipitation over the respective sub-regions were performed. Principal Component Analysis (PCA) were applied to group daily mean Sea Level Pressure (SLP) into Circulation Types (CTs). Then, PCA coupled with k-means clustering employed to regionalize precipitation fields (distributed spatially) following CTs into homogeneous climatic sub-regions. Observational data were obtained from the National Center for Environmental Prediction (NCEP) reanalysis, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS version 2), and National Meteorology Agency (NMA) of Ethiopia (gauge 1st and 2nd classes). Five principal components, which explain 98% of the total variance, were maintained using the Scree test technique. Ten CTs were obtained using positive and negative phases of each principal component scores following the extreme score values (&gt; 2 and &lt; &amp;#8722;2) procedure. From ten CTs, we found that three (CT1, CT3, and CT8) were characterized by low pressure over the southwest corner of the domain, which consequently brings rainfall over the Ethiopian highlands. The number of days classified under different CTs shows different trends. CTs seasonal distribution agreed with the regional seasons. Long-term monthly mean rainfall ranges from 0-600 mm over the region. Ethiopia is clustered into four homogeneous sub-regions based on the spatial distribution of precipitation following CTs. Rainfall from CHIRPS and gauge did not have any specific trend over the sub-regions, however high standardized anomalies were observed compared to the long term mean. The temperature showed a 2 &amp;#176;C change for the past three decades. There was a negligible difference in the shape, size, and location of regions using data from different sources. The final decision on the optimal number of homogeneous climatic sub-regions depends upon the research objective, geographical domain size, and topographic features of the domain. This study provides an assessment and decision pathway.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords: &lt;/strong&gt;climatology, regionalization, Ethiopia, precipitation, k-means, circulation types&lt;/p&gt;