The Republic of Djibouti has untapped potential in terms of renewable energy resources, such as geothermal, wind, and solar energy. This study examines the economic feasibility of green hydrogen production by water electrolysis using wind and geothermal energy resources in the Asal–Ghoubbet Rift (AG Rift), Republic of Djibouti. It is the first study in Africa that compares the cost per kg of green hydrogen produced by wind and geothermal energy from a single site. The unit cost of electricity produced by the wind turbine (0.042 $/kWh) is more competitive than that of a dry steam geothermal plant (0.086 $/kWh). The cost of producing hydrogen with a suitable electrolyzer powered by wind energy ranges from $0.672/kg H₂ to $1.063/kg H₂, while that produced by the high-temperature electrolyzer (HTE) powered by geothermal energy ranges from $3.31/kg H₂ to $4.78/kg H₂. Thus, the AG Rift area can produce electricity and green hydrogen at low-cost using wind energy compared to geothermal energy. The amount of carbon dioxide (CO₂) emissions reduced by using a “Yinhe GX113-2.5MW” wind turbine and a single flash geothermal power plant instead of fuel-oil generators is 2061.6 tons CO₂/MW/year and 2184.8 tons CO₂/MW/year, respectively.

For the first time, the spatial and temporal variability of rainfall in the Republic of Djibouti is investigated using data from 14 weather stations over the period 1946–2017. Due to limited data availability, high‐resolution long‐term satellite rainfall products (CHIRPS, PERSIANN‐CDR, TAMSATv3, ARC2) and ERA5 reanalysis also contribute to document time–space rainfall variability at monthly, seasonal and annual scales. Principal component analysis identifies two spatially coherent regions of rainfall variability in the east (coastal zone) and the west (inland zone) of the country. Annual rainfall amounts are everywhere very low (60–300 mm), but with contrasted regimes. At seasonal scale, the highest rainfall amounts in the eastern part of the country are found between October–December (OND) and March–May (MAM), while July–September is the wettest season in the western part. The monthly rainfall regimes are relatively well reproduced by most products. ERA5 displays the highest monthly correlations with observations, followed by PERSIANN‐CDR and CHIRPS. Trend analysis since 1983 shows a significant decrease of rainfall during MAM which is in agreement with other parts of East Africa. On the other hand, nonsignificant decreasing trends are observed in January–February (JF) and OND. Only June–September (JJAS) revealed a nonsignificant increasing trend, but it follows a prior drying trend since the 1950s. The impact of large‐scale background climate on rainfall variability is assessed with focus given on El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Rainfall variability during OND shows a significant correlation with IOD, while in JJAS it is significantly negatively correlated with ENSO. In general, ERA5, CHIRPS and PERSIANN datasets are best able to reproduce rainfall patterns in Djibouti and suitable for further analysis. The fact that the interannual and decadal‐scale rainfall variations in Djibouti show large‐scale teleconnections with global sea‐surface temperature fields, as demonstrated in this study, provides good prospects for the prediction of rainfall variations at a range of different temporal scales.

Drought is a meteorological and hydrological phenomenon affecting the environment, agriculture, and socioeconomic conditions, especially in arid and semi-arid regions. A better understanding of drought characteristics over short and long timescales is therefore crucial for drought mitigation and long-term strategies. For the first time, this study evaluates the occurrence, duration, and intensity of drought over the Republic of Djibouti by using a long-term (1961–2021) rainfall time series at Djibouti Airport, completed by the CHIRPS precipitation product and local records from 35 weather stations. The drought is examined based on the Standardized Precipitation–Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) at 3-, 6-, 9-, 12-, and 24-month timescales, so as to document short-, medium-, and long-duration events. The SPEI and SPI showed a significant drying tendency for the indices computed over 12 and 24 months at Djibouti Airport. The eastern coastal region of the Republic of Djibouti was the most affected by the increased drought incidence in recent decades, with more than 80% of the extremely and severely dry events occurring within the period 2007–2017. In contrast, the western regions recorded a positive trend in their SPIs during the period 1981–2021, due to the dominance of the June–September (JJAS) rains, which tend to increase. However, in the last few decades, the whole country experienced the droughts of 2006/2007 and 2010/2011, which were the longest and most intense on record. Large-scale climate variability in the Indo-Pacific region partially affects drought in Djibouti. The SPI and SPEI are significantly positively correlated with the Indian Ocean Dipole during October–December (OND), while for JJAS the SPI and SPEI are negatively correlated with Nino3.4. The wet event in 2019 (OND) causing devastating floods in Djibouti city was linked with a positive IOD anomaly. This study provides essential information on the characteristics of drought in the Republic of Djibouti for decision-makers to better plan appropriate strategies for early warning systems to adapt and mitigate recurrent droughts that put the country’s agro-pastoral populations in a precarious situation.

The Republic of Djibouti is a small country in an arid context coupled with a high variability of rainfall that generates flash floods causing severe damage to the population and infrastructure. The mechanisms controlling extreme rainfall events in this part of the Horn of Africa remain poorly understood. In this study, we document the atmospheric circulation patterns associated with such events. To that end, we use rain‐gauge data (a network of 36 stations on the period 2013–2020), satellite‐based rainfall estimates (CHIRPS, IMERG, MSWEP and RFE) and atmospheric reanalyses (ERA5), all at the daily timescale, over their common period 2001–2020. A multivariate Agglomerative Hierarchical Clustering of rainy days in Djibouti (≥10% of grid‐points exceeding 1 mm·day⁻¹, according to all four satellite products) reveal four clusters, which differentiate from each other by the intensity and spatial extent of rainfall. These clusters show a nonhomogeneous seasonal distribution, occurring mainly in the March–April–May (MAM) and July–August–September (JAS) seasons, and more rarely in October–November–December (OND). The atmospheric circulation anomalies associated with the clusters are quite similar and highly season‐dependent. In MAM most clusters display an anomalous trough over the Red Sea, from 700 to 200 hPa. In JAS, an anomalous low over the southern Red Sea drives a thicker than normal monsoon flow at 700 hPa, while upper northerlies prevail at 200 hPa. In OND, most rainy events result from moisture advection from the Western Indian Ocean, favoured by positive phases of the Indian Ocean Dipole. Some highly unusual atmospheric circulation patterns (e.g., associated with tropical cyclones) can also result in intense rainfall events in Djibouti. These findings provide new insights on the physical causes of extreme events in the Horn of Africa, with applications to improved early warning and skill evaluation of climate models for climate change projections.