Ethiopia is located in the Horn of Africa within 3˚N - 15˚N and 33˚E - 48˚E, bordered with Eritrea to the north, Djibouti to the east, Sudan to the west, Kenya to the south, and Somalia to the south and east. It covers an area of about 1.14 million square kilometers (944,000 square miles), the country’s topography is characterized by high and rugged plateaus and peripheral lowlands. Elevation in the country range from 160 meters below sea level (northern exit of the Rift Valley) to over 4600 meters above sea level (of northern mountainous regions) (Figure 1). The highest mountains are concentrated on the northern and southern plateaus of the country [23].

Ethiopia has three climatological rainy seasons, namely February-May (FMAM, known as Belg in Ethiopia), June-September (JJAS, known as Kiremt), and October-January (ONDJ, known as Bega) [24] - [31]. Figure 1 indicates that JJAS is the main rainy season, accounting for 50% - 80% of annual rainfall totals and the largest seasonal rainfall for many portions of the country [19]. The seasonal rainfall is highest over northwestern, western, southwestern, and central portions of Ethiopia (Figure 1). As Cleary document by [19], the National Meteorological Agency (NMA) has issued seasonal forecasts three times a year since 1987 by assessing the time-lag correlations of SSTs with seasonal rainfalls of various regions over Ethiopia. The seasonal probability forecasts of four-month seasonal rainfall totals are provided with typically one-month lead time (e.g. the forecast for June-September [JJAS] is issued in late May).

In this study, forecasts are derived from the various dynamical model outputs by applying post-processing based on Canonical Correlation Analysis (CCA) [39]. The predictor (i.e. dynamical model) spatial domain is usually designed to be larger than the predictand (forecast) domain, so that relevant features outside of the targeted domain can be used for better model error correction, for example, to correct for systematic errors in model spatial structure of circulation and related variables (Figure 1). In the CCA method used here, preorthogonalization, using empirical orthogonal function (EOF) analysis, is done separately on the model hindcasts (the X variable, or predictor) and on the corresponding observations (the Y variable, or predictand), and a truncated set of the principal component time series from these EOFs is used as input to the CCA. The main purpose of the EOF/PCA analyses is to reduce the potential for over-fitting, through data compaction and noise filtering [40].

This section provides both deterministic and probabilistic skill assessment of the NMME system. We utilize both deterministic and probabilistic methods of assessment as there is evidence that the two approaches together provide a more complete representation of skill [41]. The forecast skill of NMME models is evaluated by using several deterministic skill measures: two alternative forced choice (2AFC), Spearman, Pearson correlation, and ROC [42]. The Spearman rank and Pearson correlation is computed for the NMME mean precipitation forecast at one-month lead-time before the start of JJAS season. The NMME mean represents the ensemble mean of the post-processed individual NMME model forecasts. Both correlations are computed separately for the JJAS season and at one-month lead-time, for the hindcast period of 1982-2010. According to [43] reported that the sign of the correlation coefficient determines whether the correlation is positive or negative and the magnitude of the correlation coefficient determines the strength of the correlation. Moreover [43] suggests the correlation categories 0.00 - 0.19: “very weak”, 0.20 - 0.39: “weak”, 0.40 - 0.59: “moderate”, 0.60 - 0.79: “strong” and 0.80 - 1.0: “very strong”. We applied root mean square error (RMSE) (also called the root mean square deviation [RMSD]) to measure the difference between modeled and observed variables.

A Taylor diagram summarizes the statistical relationship between model and the observed/reference field [44]. The diagram is useful for evaluating the accuracy of multiple model outputs against a reference data. Further information on the Taylor diagram is provided in the supplemental material.

We first analyzed the spatial seasonal rainfall distribution of rainfall climatology for Ethiopia using CHIRPS data. Rainfall over the tropical semi-arid and arid areas was characterized by high inter-annual variability [45]. Therefore, understating the seasonal rainfall performance is important for agriculture, water, energy and for other socio-economic activities. In Ethiopia, June-September is the main rainy season, and the magnitude of rainfall is higher over most parts of the country. The June-September rainy season is very important for its water resources management and agriculture production. Figure 2 shows that northwestern, western, and central parts of the country exhibit the highest rainfall during this season, and the spatial averaged daily rainfall amount varies between 0 to 10 mm/day and exceeds 10 mm over the western portions of Ethiopia. The daily mean spatial rainfall pattern obtained from ensemble NMME models hindcasts shows similarity with CHIRPS over most parts of the country, but the models slightly underestimate rainfall over northern parts of Ethiopia.

In this section we summarize the deterministic verifications methods utilized. The accuracy of the predictions can be indicated by their correlation coefficient, standard deviation and root mean square error with respect to the observations, over the 1982-2010 period. The root mean square error is defined as the average, for predictions for a given target month and lead time, of the squared differences between the predictions and their corresponding observations. The skill of the NMME models, in terms of spatially averaged correlation coefficient, root mean square error, and standard deviation metrics, are presented in Figure 3. The

correlation skill of NMME models varies between 0.1 and 0.68 (Figure 3). The GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012 models exhibit relatively high skill (0.68, 0.58, 0.52 and 0.5, respectively). The COLA-RSMAS-CCSM4, CMC1-CanCM3 and NCEP-CFSv2 models exhibit correlations of <0.3, and these models have limited skill in simulating and predicting JJAS seasonal rainfall over Ethiopia (Figure 3).

The two alternatives forced choice (2AFC) score and relative operating characteristics (ROC) score for above and below are presented in the following sections. The NMME model forecast skill for JJAS seasonal precipitation is not equally distributed over Ethiopia (Figures 4-6). The red colors, which characterize the north, northwest, northeast, central and eastern portions of Ethiopia, depict where NMME skill is higher in capturing JJAS seasonal rainfall, and blue areas indicate the opposite. The 2AFC result of individual NMME models depicts that skill difference over most parts of Ethiopia in predicting the June-September seasonal rainfall (Figure 4). The 2AFC skill of the GFDL-CM2p5-FLOR-A06, CMC2-CanCM4 and GFDL-CM2p5-FLOR-B01 models are skillful over northern, central and southwestern portions of Ethiopia and these models depict that limited skill over western and southeastern portion of Ethiopia. The models NASA-GMAO-062012, COLA-RSMAS-CCSM4, CMC1-CanCM3 and NCEP-CFSv2 show comparably less skill over most parts of Ethiopia. Generally, the 2AFC scores of the NMME results show limited skill in predicting the June-September seasonal rainfall over Ethiopia across the western region of the country. It is also noted that most models predict worse than climatology over

western and southeastern portion of Ethiopia.

The variability of seasonal rainfall over Ethiopia leads to the recurrence of extreme drought and flood, and the intensity of extreme events affects various socio-economic activities of the country [48]. Ethiopia is one of the largest humanitarian aid beneficiaries in the world and experiences significant climate induced drought and water related stresses on crop and livestock productivity [49]. Every year approximately three million Ethiopians are affected by crop production shortfalls adding to the 7.6 million supported every year by the

Productive Safety Net Program a social safety net supporting some of Ethiopia’s poorest and most food-insecure families [50] because of extreme drought. Very recently the 2015 El Niño-induced drought has caused food insecurity among 10.2 million people one of the highest on the record [1]. It is thus very important to analyze extreme events [51]. Particular example in Ethiopia is droughts and flood continuously occurred over northeastern, eastern, central and northern parts of the country and recently become an annual problem for loss of lives and properties [48].

Accurate and reliable prediction of the seasonal rainfall prior few months in advance can assist in anticipating extreme events such as drought. We have chosen to focus on the extreme drought year of 2015 in order to examine the skill of the NMME system in capturing the dry extreme event. Figure 9 indicates that

the NMME models predict a below normal probability category rather than above normal. The models capture more than 85% of the area that experienced below normal rainfall over northern, northeastern, central, and eastern parts of Ethiopia during the strong El Niño year of 2015 in the JJAS season. Due to this most parts of Ethiopia affected by drought extremes in 2015. So, NMME based forecasting is skillful to detect the drought extremes over Ethiopia. Previous studies of [52] revealed that NMME models had a higher skill to predict the seasonal drought extremes. Moreover, [15] used seasonal precipitation forecasts from the NMME and other GCMs to examine the predictability of drought onset around the globe.