# The evolution of drought characteristics in semi-arid Africa over the last four decades

Komi Mawulom Claude Afamondji, Alyson Brayner Sousa Estacio, Ismail Mohsine, Nourelhouda Karmouda, Tarik Bouramtane, Mounia Tahiri, Ilias Kacimi, Sarah Tweed, Marc Leblanc

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## To cite this version:

Komi Mawulom Claude Afamondji, Alyson Brayner Sousa Estacio, Ismail Mohsine, Nourelhouda Karmouda, Tarik Bouramtane, et al.. The evolution of drought characteristics in semi-arid Africa over the last four decades. Journal of Hydrology: Regional Studies, 2026, 63, pp.103087. 10.1016/j.ejrh.2025.103087. hal-05470191

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Submitted on 21 Jan 2026

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# The evolution of drought characteristics in semi-arid Africa over the last four decades

Komi Mawulom Claude Afamondji<sup>a</sup>, Alyson Brayner Sousa Estacio<sup>b,c</sup>, Ismail Mohsine<sup>a</sup>, Nourelhouda Karmouda<sup>a</sup>, Tarik Bouramtane<sup>a ID</sup>, Mounia Tahiri<sup>a</sup>, Ilias Kacimi<sup>a</sup>, Sarah Tweed<sup>a,d</sup>, Marc Leblanc<sup>a,d,e,*</sup>

## ARTICLE INFO

**Keywords:**

- Drought
- African semi-arid
- Duration
- Intensity
- Severity
- SPEI
- SPI

## ABSTRACT

**Study region:** Semi-arid Africa, covering six subregions: the Mediterranean (MED), Sahel, North Eastern Africa (NEAF), South Eastern Africa (SEAF), Southern Africa (SAF), and Madagascar (MDG).

**Study focus:** We analyse drought duration, intensity, and severity from 1979 to 2024 across semi-arid Africa. Using Climate Prediction Center (CPC, 0.5°) precipitation and temperature, we compute the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Trends are detected with the Mann-Kendall test and Theil-Sen slope estimator. Short-term droughts (3–6 months), strongly influenced by temperature variability, are distinguished from 12-month events driven by cumulative hydrological deficits.

**New hydrological insights:** Three drought episodes emerge: the early 1990s, early 2000s, and the recent period beginning in 2022. Long time-scale indices (SPI-12, SPEI-12) capture the most persistent droughts, whereas short time-scale indices (SPEI-3, SPEI-6) reveal intense temperature-driven episodes. In MED, only 7–25 % of grid cells show significant SPI trends in duration, severity, and intensity, but up to 75 % exhibit drought intensification with SPEI, underscoring strong temperature sensitivity. Across NEAF, SEAF, SAF, and MDG, 25–55 % of pixels show significant increases in drought duration (up to +3 months per decade), severity (+3 units per decade), and intensity (+0.5 units per decade). Parts of the western Sahel and southern Madagascar display decreasing trends. Overall, the study delivers a continent-wide assessment of drought evolution and identifies hotspots where intensifying drought threatens water resources and food security.

## 1. Introduction

Drought is a slow-onset natural hazard characterized by a prolonged period of water deficit, typically driven by below-average

<footnote><sup>a</sup> Geosciences, Water and Environment Laboratory, Faculty of Sciences, Mohammed V University in Rabat, Avenue Ibn Batouta, Rabat, Morocco</footnote>

<footnote><sup>b</sup> Research Institute for Meteorology and Water Resources (FUNCEME), Fortaleza, Ceará, Brazil</footnote>

<footnote><sup>c</sup> University for International Integration of the Afro-Brazilian Lusophony (UNILAB), Redenção, Brazil</footnote>

<footnote><sup>d</sup> UMR G-EAU, IRD, Montpellier, France</footnote>

<footnote><sup>e</sup> Hydrogeology Laboratory, UMR EMMAH, University of Avignon, Avignon, France</footnote>

<footnote>* Corresponding author at: Hydrogeology Laboratory, UMR EMMAH, University of Avignon, Avignon, France.
E-mail address: Marc.leblanc@univ-avignon.fr (M. Leblanc).</footnote>
precipitation and heightened atmospheric evaporative demand (Ding et al., 2022; Zhou et al., 2024). It affects not only meteorological conditions but also disrupts soil moisture levels, streamflow, vegetation health, and human systems (Wei et al., 2024). Droughts manifest in various forms meteorological, agricultural, hydrological, and socio-economic, each representing distinct dimensions of water scarcity and presenting specific challenges for monitoring and mitigation (Haile et al., 2020; Ogunrinde et al., 2025).

Over the past four decades, African droughts have been the subject of ongoing debate about their regional trajectories and climatic drivers. In the Sahel, several studies reported a partial rainfall recovery since the mid-1980s, following the severe droughts of the 1970s–1980s, though this recovery remains spatially inconsistent and coincides with rising temperatures that enhance atmospheric water demand (Tall et al., 2023). In contrast, Southern and Eastern Africa have experienced more persistent drought conditions, influenced by increased evapotranspiration and warm phases of ocean–atmosphere phenomena such as ENSO and the Indian Ocean Dipole, which strongly modulate rainfall variability across the continent (Haile et al., 2020; Palmer et al., 2023; Chivangulula et al., 2025). These contrasts underscore the complex and regionally diverse response of African semi-arid systems to climate variability and warming.

Unlike rapid-onset disasters, droughts evolve gradually but can lead to widespread and long-lasting impacts on agriculture, water availability, ecosystems, and livelihoods (Cavus and Aksoy, 2020; Perez et al., 2024). In recent decades, drought has become one of the most critical environmental threats of the 21st century, with its frequency, intensity, and spatial extent projected to rise under future climate scenarios (Brodribb et al., 2020; Wei et al., 2024). Estimates suggest that extreme drought currently affects 2–6% of global land areas, while severe-to-extreme drought can impact up to 16% (Kogan and Guo, 2016). These events pose growing risks to food security, public health, and socioeconomic stability (Salvador et al., 2023; Walker and Van Loon, 2023). Climate projections further indicate that nearly two-thirds of the world's population will experience intensified drought conditions in the coming decades, particularly in already vulnerable regions such as Africa, Australia, southern Europe, and the Americas (Naumann et al., 2018; Zhou et al., 2024).

Drought stands out as one of the most critical environmental challenges in Africa, severely affecting water availability, agricultural productivity, ecosystem services, and socioeconomic stability (Amazirh et al., 2023; Lombe et al., 2024). Its impacts typically follow a cascade of propagation, beginning with rainfall deficits and progressing through soil moisture depletion, vegetation stress, and reductions in total water storage (Amazirh et al., 2023). These effects are particularly amplified in already vulnerable regions such as the Sahel, Central Africa, and Southern Africa, where drought frequency and severity are increasing (Tall et al., 2023). Despite the continent's diverse climatic zones, alarming patterns are emerging, including intensifying dry spells, disrupted hydrological cycles, and declining vegetation cover (Lim Kam Sian et al., 2023; Perez et al., 2024).

Africa's semi-arid zones, stretching from the Sahel to the Horn of Africa, including southern Madagascar and the southern Africa highlands, are especially exposed to the impacts of climate change. Already marked by low and erratic rainfall, these areas are projected to experience more frequent and intense droughts over the coming decades (Schwabe et al., 2015). The ecosystems in these regions are fragile and highly sensitive to the combined pressures of human activity and climate dynamics. Agricultural expansion in such marginal environments further exacerbates land degradation and ecological stress (Huang et al., 2016). In North Africa and the Mediterranean, drought severity is expected to intensify, with evidence of unprecedented events over the past millennium (Cook et al., 2016; Mathbout et al., 2021). Southern Madagascar faces delayed rainy season onset and prolonged dry periods, compounded by hydrogeological limitations and rising temperatures (Randriamarolaza et al., 2022; Rigden et al., 2024). In Southern Africa, ENSO-related ocean–atmosphere interactions have increased drought recurrence, severely impacting crop yields and urban water supply (Chivangulula et al., 2023; Rusca et al., 2023).

Given their high sensitivity to rainfall variability, wide geographical distribution across the continent, and the large populations that depend on them, semi-arid regions in Africa require focused scientific attention (Schwabe et al., 2015; Yahaya et al., 2024). Despite growing research efforts, our understanding of how key drought characteristics such as duration, severity, and intensity evolve over time and space within these regions remains partial and fragmented. Most existing studies have provided valuable insights at national or subregional scales (Adejuwon and Dada, 2021; Akinyemi, 2021; Matimolane et al., 2024; Tegegne et al., 2025), while others have assessed drought trends at the scale of the entire continent without specifically isolating semi-arid zones (Spinoni et al., 2014; Tall et al., 2023). As a result, there is a lack of a coordinated and comprehensive assessment that captures the full diversity of drought dynamics in these vulnerable areas. This limits our capacity to consistently monitor, anticipate, and respond to drought impacts in an effective and regionally adapted manner.

To address this, assessing the spatiotemporal variability of drought in Africa's semi-arid zones requires the use of standardized and robust drought indices. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are among the most commonly applied indicators due to their simplicity, multiscale applicability, and statistical robustness (Ndayiragije and Li, 2022). SPI measures precipitation anomalies alone, while SPEI incorporates both precipitation and atmospheric evaporative demand through potential evapotranspiration (PET), making it particularly sensitive to the effects of global warming (Vicente-Serrano et al., 2010).

Building on these observations, the present study aims to comprehensively assess the spatiotemporal variability of drought across Africa's semi-arid regions. Specifically, it focuses on analyzing how the three key drought characteristics, duration, severity, and intensity, have evolved over the past four decades. The analysis is carried out at two levels: first, by examining all semi-arid zones collectively to capture the general drought behavior across the continent; and second, by conducting a focused assessment within six distinct subregions, namely the Mediterranean (MED), the Sahel, North Eastern-Africa (NEAF), South Eastern-Africa (SEAF), Southern Africa (SAF) and Madagascar (MDG), to better understand regional drought patterns and their evolution. This dual-scale approach allows for a nuanced interpretation of both global and localized drought dynamics. Furthermore, the study evaluates the performance of the Standardized Precipitation Index (SPI) and, in particular, the Standardized Precipitation Evapotranspiration Index (SPEI), which
remains underutilized in semi-arid African contexts. The objective is to determine the extent to which these indices effectively capture
spatial and temporal drought variability, and to assess the statistical significance and relevance of each index across the different
regions. Our analysis is limited to CPC data. It was beyond the scope and time allocated to our project to analyse other datasets. CPC
data, which are based on observational datasets derived from gauge data and geostationary satellite data, are amongst the most
commonly used climate data set and have been the subject of many validation (Carvalho et al., 2012; Chen and Knutson, 2008; Dinku
et al., 2008; Hermance and Sulieman, 2014; Naumann et al., 2014; Negrón Juárez et al., 2009; Sawunyama and Hughes, 2008; Xie
et al., 2017). A detailed presentation of the CPC data and why it was selected for this study is presented in the next section.

## 2. Data and methods

### 2.1. Study area

This study focuses on Africa’s semi-arid zones, defined based on the aridity classification of Zomer et al. (2022), where the aridity index (AI) ranges between 0.2 and 0.5 (Fig. 1). To capture the spatial variability of drought dynamics, a pixel-based and a regional aggregated analysis were applied across all identified semi-arid areas, which were then grouped into six major climatic subregions: the Mediterranean region (MED), the Sahel and North Eastern-Africa (NEAF), South Eastern-Africa (SEAF), Southern Africa (SAF), and Madagascar (MDG) (Fig. 1). This geographical framework enables a comprehensive assessment of the spatial and temporal evolution of drought characteristics across the African semi-arid domain.

Each subregion displays distinct hydroclimatic features. MED, Semi-arid conditions are shaped by declining rainfall linked to zonal wind patterns and the high sensitivity of Mediterranean watersheds to drought. Mountain systems play a key role in regulating runoff

<figure><img src="image_3.png" /><figcaption>Fig. 1. Map of the aridity index for arid and semi-arid regions across the Africa (Zomer et al., 2022).</figcaption></figure>
and water availability. Sahel region, it's a zone exhibits a steep latitudinal rainfall gradient, with annual precipitation decreasing from 450 to 500 mm in the south (around 15°N) to less than 200 mm in the north (17°N), largely governed by the migration of the Intertropical Convergence Zone (ITCZ) (Wang et al., 2005; Zeng, 2003). Historical observations show a long-term decline of nearly 100 mm/year between the periods 1930–1965 and 1966–2000 (Djomou et al., 2009; Huang et al., 2009; Maranz, 2009).

<figure><img src="image_4.png" /><figcaption>Fig. 2. Methodology flowchart.</figcaption></figure>
NEAF region, particularly in Ethiopia and parts of Sudan, the region is marked by high rainfall variability and complex drought behavior. Shifts in precipitation regimes have led to agricultural losses, with yield reductions reaching 40% during prolonged dry periods (Kassaye et al., 2021). While drought trends are less evident in parts of Central Africa, growing climate variability, especially in the Congo Basin, poses risks to water resources and food security (Karam et al., 2023). SAF region, encompassing both Western and Eastern Southern Africa (WSAF and ESAF), this region is characterized by strong interannual rainfall variability and the influence of large-scale atmospheric drivers such as El Niño (Afamondji et al., 2025; Maure et al., 2018). In WSAF, aridity thresholds are shifting eastward due to strengthened westerly airflows. ESAF, in contrast, faces increased desiccation from rising temperatures and localized rainfall anomalies (Afamondji et al., 2025; Demissie et al., 2024). MDG, precisely the southern Madagascar, though relatively understudied, presents semi-arid conditions exacerbated by warming trends, decreasing precipitation, and frequent droughts that severely affect ecosystems and livelihoods (Afamondji et al., 2025).

## 2.2. General framework

The study follows a structured framework integrating climate data processing, drought index calculation, and statistical assessment of drought behavior across African semi-arid regions (Fig. 2). The approach comprises the computation of drought indices (SPI and SPEI) at multiple accumulation scales, the identification and characterization of drought events, and the analysis of temporal trends in drought properties. This framework enables a consistent evaluation of both precipitation-driven and climate-driven drought processes under regional semi-arid conditions. The description of climate data and the methodological details for each step are presented in the following sections.

## 2.3. Climate data

The Climate Prediction Center (CPC) provides globally consistent datasets of precipitation (P) and surface air temperature (T) that are extensively used in hydroclimatic analyses and drought research (Xie et al., 2007; Fan and van den Dool, 2008). The CPC Unified Gauge-Based Analysis of Daily Precipitation delivers global daily rainfall estimates from 1979 onward at 0.5° spatial resolution, derived primarily from quality-controlled gauge networks (Chen et al., 2002). While CPC P and T daily data are primarily gauge-based, the documentation indicates that satellite observations can be part of the pipeline. Satellite-derived products, such as CMORPH (Joyce et al., 2004) and CMAP (Xie and Arkin, 1997), are incorporated to enhance spatial completeness and reduce interpolation bias in data-scarce regions. The integration of passive microwave and infrared satellite observations allows for improved depiction of convective rainfall and gap-filling where in situ data are limited (Yin et al., 2004). CPC data also relies on of near-real-time infrared data merged on a global scale from the approximately 11-µm infrared channels on geostationary satellites GMS-5, GOES-8, GOES-10, Meteosat-7 and Meteosat-5 (Janowiak et al., 2001). On the daily scale, the CPC precipitation dataset ranks first, followed by IMERG, TMPA, and ERA5 in decreasing order of accuracy (Jiang et al., 2023).

For temperature, the CPC Global Daily Temperature dataset provides maximum and minimum air temperatures from 1948 to present on a comparable 0.5° grid (Fan and van den Dool, 2008). Although primarily station-based, the dataset benefits indirectly from satellite and reanalysis inputs used for bias correction and spatial interpolation in poorly monitored areas (Vose et al., 2012; Lawrimore et al., 2011).

The combined use of gauge and satellite observations ensures spatially coherent and temporally consistent climate fields, essential for detecting trends in drought characteristics. This is particularly relevant in semiarid regions of Africa, where sparse observational networks limit the reliability of purely in situ datasets (Nicholson et al., 2018; Maidment et al., 2015). The long temporal coverage, high temporal resolution, and homogeneity of CPC products make them well suited for assessing changes in drought frequency, duration, and intensity (Sheffield et al., 2014; Ziese et al., 2014).

Across Africa, CPC data quality varies with region and topography. In East Africa, CPC-based algorithms moderately underestimate precipitation in highland areas and overestimate it in arid regions, mainly due to sub-cloud evaporation (Dinku et al., 2011). Earlier evaluations found that CPC merged analyses (CMAP) performed reasonably well in complex terrains, though challenges remain for validation in mountainous regions (Dinku et al., 2007). Over mountainous African terrain, CPC precipitation products showed strong correlations ($r \approx 0.90-0.95$) with gauge networks and relatively low systematic errors, performing best during wet seasons (Dinku et al., 2008). In West Africa, CPC-FEWS data, which incorporate gauge observations, outperformed global satellite rainfall products in the Senegal River basin, achieving $R^2$ values of 0.84-0.87 after bias correction, comparable to gauge-based estimates (Stisen and Sandholt, 2010). More than In West African monsoon regions, global climate models show typical positive biases over the Guinea Coast and negative biases in the Sahel, attributed to southward-shifted Intertropical Convergence Zone positioning and tropical Atlantic sea surface temperature biases (Paxian et al., 2016). In the Sahel and Madagascar, there is a limited number of gauged data available (Chen et al., 2008). Analyses based on relatively low-density gauge networks tend to generate larger biases. The frequency of dry days is only about 32%, representing less than half of that recorded at in situ stations, while the occurrence of low precipitation events ($R < 1$ mm day$^{-1}$) is approximately three times higher than observed (Chen et al., 2008). However the satellite precipitation estimates show systematic bias and developed a grid-based merging procedure using CPC daily rain gauge analysis to correct these biases, indicating CPC data serves as a reliable ground-truth reference (Boushaki et al., 2009). Observations from multiple quality-controlled in situ stations were integrated into CPC datasets, whose reliability has been demonstrated in previous studies (Fan and van den Dool, 2008; Preethi et al., 2011; Xie et al., 2007). Thus, daily maximum and minimum temperature data and daily precipitation data from the CPC are used in this study, on a 0.5° × 0.5° grid over a 46-year period (1979-2024).
## 2.4. Drought indices

Drought conditions were assessed using the Standardized Precipitation Index, SPI (McKee et al., 1993) and the Standardized Precipitation Evapotranspiration Index, SPEI (Vicente-Serrano et al., 2010). SPI was calculated solely from precipitation, while SPEI integrated both precipitation and potential evapotranspiration (PET), thereby incorporating the influence of temperature.

To compute the indices, daily precipitation and PET data were accumulated on three scales (3, 6, and 12 months), in order to capture the dominant patterns of variability: intra-annual variability at the 3- and 6-month scales, and interannual variability at the 12-month scale. For SPI, the aggregated precipitation series was fitted to a Gamma distribution, corrected for zero precipitation, and standardized to obtain normally distributed values (Guttman, 1999). For SPEI, the climatic water balance (P – PET) time series was fitted to a log-logistic distribution and then standardized to a normal distribution. Differently from SPI, log-logistic distribution is a more robust fit for water balance anomalies (Vicente-Serrano et al., 2010). Indeed, previous research has demonstrated its superior ability to produce normalized SPEI series (mean = 0, standard deviation = 1) compared to other probability distributions such as the Generalized Extreme Value (GEV), log-normal, and Pearson Type III distributions (Beguería et al., 2014; Gebrechorkos et al., 2023; Lee et al., 2024).

For the SPEI computation, PET was estimated using the Thornthwaite method (Thornthwaite, 1948). Despite its known tendency to underestimate evapotranspiration, the Thornthwaite method is widely applied in semi-arid regions, because of its computational simplicity and minimal data requirements, relying only on mean monthly temperature, which makes it particularly valuable for data-scarce environments, semi-arid Africa (Al-Sudani, 2019; Pei et al., 2019). Compared to more complex methods such as the Penman-Monteith, Thornthwaite provides a cost-effective and time-efficient approach for large-scale drought monitoring, maintaining strong empirical relationships between temperature and evapotranspiration - $R^2 = 97.45–99.84 \%$ (Al-Sudani, 2019).

While the Penman-Monteith method generally outperforms Thornthwaite due to its inclusion of additional meteorological variables (Li et al., 2019), several studies have demonstrated that Thornthwaite-based SPEI remains robust in representing drought frequency, duration, and spatial variability in semi-arid regions (Kumari et al., 2024; Worku, 2024; Zhou et al., 2020). Comparative analyses further show that discrepancies between Thornthwaite and Penman-Monteith become negligible for longer accumulation periods than one year (Lee et al., 2024).

Moreover, both SPI and SPEI were employed because these indices differ in their sensitivity to temperature and enable a clearer assessment of drought responses to warming and climate change. While both indices exhibit strong correlations across multiple timescales (Filipović et al., 2025; Tefera et al., 2019), SPEI consistently demonstrates greater responsiveness to climatic warming due to its incorporation of potential evapotranspiration (PET) (Wang et al., 2025). This inclusion allows SPEI to capture temperature-driven drought intensification that SPI, which relies solely on precipitation, cannot detect (Li et al., 2020).

<figure><img src="image_5.png" /><figcaption>Fig. 3. Schematic representation of drought characteristics for SPI and SPEI.</figcaption></figure>
## 2.5. Identification and characterization of drought events

Drought events were identified as periods during which the values of the SPI or SPEI indices remained continuously below zero (drought threshold), indicating persistent dry conditions as described by Filho et al. (2020). For each drought event, three key characteristics were calculated to quantify its impact: duration, which refers to the number of consecutive months with negative index values, severity, representing the cumulative sum of the negative index values over the event period and intensity, defined as the average negative index value throughout the event. These metrics provide a comprehensive description of drought behavior (Misnawati et al., 2021) (Fig. 3).

To assess interannual variability, these metrics were aggregated to the yearly scale. Following Spinoni et al. (2014) and Ogunrinde et al. (2025), annual duration was defined as the sum of all drought durations within a year, annual severity as the sum of event severities, and annual intensity as the mean of event intensities (Fig. 3). For multi-year droughts, the data were subdivided by year in order to examine interannual variability and to compute spatial decadal trends. This approach inherently constrains the spatial representation of drought duration to a maximum of 12 months; however, the temporal analysis enables the assessment of multi-year variability and long-term temporal dynamics (Fig. 3).

## 2.6. Trend detection

To evaluate long-term changes in drought characteristics (duration, severity, intensity), we applied the Mann-Kendall (MK) test (Kendall, 1975; Mann, 1945) and the Theil-Sen slope estimator (Sen, 1968). The MK test is a non-parametric method widely used for detecting monotonic trends in hydroclimatic series, as it does not assume any particular data distribution. The test statistic S is given by Eq. 1.

$$S = \sum_{i=1}^{n-1} \sum_{i=1}^{n} \operatorname{sgn}(x_j - x_i) \quad (1)$$

where sgn(·) represents the sign function and $x_1, ..., x_n$ is the sequence of annual drought characteristics (i.e., duration, severity and intensity). A positive (negative) value of S indicates an increasing (decreasing) trend. The significance of the trend was assessed using the standardized normal statistic Z, assuming the null hypothesis of no trend. The tests were applied for each pixel, adopting a 95 % significance level.

Prior to the application of MK test, an autocorrelation analysis of annual drought characteristics was conducted to assess potential serial dependence in the data. The analysis revealed no significant autocorrelation, and therefore, the original (non-modified) MK test was applied to evaluate monotonic trends. The results of the autocorrelation analysis are provided in the Supplementary Material (Figure S9-S14). Then, the MKTheil-Sen estimator was used to quantify the magnitude of detected trends (Wan et al., 2023). For a time series $x_1, x_2, ..., x_n$ of annual drought characteristics at times $t_1, t_2, ..., t_n$, the slope ($\beta$) is given by the Eq. 2.

$$\beta = \operatorname{median}[(x_j - x_i)/(t_j - t_i)] \quad \text{for all } 1 \le i < j \le n \quad (2)$$

In this study, $\beta$ was expressed in specific units for each drought characteristic: months per decade for duration, and dimensionless values per decade for severity and intensity. These units provide an interpretable measure of how drought characteristics are expected to change over a ten-year period.

## 2.7. Spatial representation and analysis strategy

To ensure a comprehensive assessment of drought dynamics, both pixel-based and regionally aggregated analyses were conducted across all identified semi-arid zones in Africa. These semi-arid areas were grouped into six major climatic subregions: the Mediterranean region (MED), the Sahel and North Eastern-Africa (NEAF), South Eastern-Africa (SEAF), Southern Africa (SAF), and Madagascar (MDG) (Fig. 1).

In the pixel-based approach, all computations were performed independently for each grid cell located within the semi-arid domain of the respective subregion. This included the calculation of SPI and SPEI, the identification of drought events, the derivation of drought characteristics (duration, severity, and intensity), and the application of trend detection tests (Mann-Kendall and Theil-Sen). This procedure allowed the spatially explicit evaluation of drought patterns and their temporal evolution.

Given the large number of simultaneous tests, this approach inherently involves a multiple testing issue, which may increase the likelihood of false positives when interpreting spatial patterns. This issue is explicitly considered in the interpretation of the results, emphasizing spatial consistency and the robustness of regional trends over isolated significant pixels.

In the regionally aggregated analysis, SPI and SPEI values from all selected pixels within each subregion were first spatially averaged over the study period 1979–2024 to generate representative regional time series. These aggregated series were then used to identify drought events, and determine their main characteristics, following the same methodological framework applied at the pixel level. This strategy enabled the assessment of large-scale regional behavior while minimizing local noise.

# 3. Results

Subsequently, we analyze the results derived from spatial maps and histograms of p-values and Sen's slopes applied to drought
characteristics in semi-arid regions of the African continent (MED, Sahel and NEAF, SAF and SEAF, and MDG).

## 3.1. Continental scale evolution of drought characteristics across semiarid Africa

To initiate our analysis, we examine the temporal evolution of the drought indices SPI and SPEI across all Africa's semi-arid zones. The semiarid pixels, defined by the aridity classification of Zomer et al. (2022), were averaged across our six zones, over the study period 1979–2024. Both SPI-3 and SPEI-12 reveal a major drought event in the early 1990s (Fig. 4a and f), followed by a pronounced drought period in the early 2000s as indicated by SPI-6 and SPI-12 (Fig. 4b and c). SPEI-3 and SPEI-6 show a clear intensification of dry conditions starting in 2022 (Fig. 4d and e). These major drought events are defined as those exhibiting the longest drought duration (SPI or SPEI <0) within the chronological record spanning from 1979 to 2024 (For SPI-3 it is 20 months, SPI-6 is 48 months, SPI-12 is 72 months, SPEI-3 is 30 months, SPEI-6 is 26 months and SPEI-12 is 71 months) (Adisa et al., 2020).

The regional validation of major drought periods confirms that, when considering all six regions, the principal drought episodes correspond to the 1990s, the 2000s, and the early 2020 s (notably 2022). In particular, the MED region has experienced prolonged droughts since the early 2020 s (Figure S1). A similar pattern is observed in the MDG region, which also exhibits significant drought episodes during the 1990s (Figure S6). The Sahel region is characterized by major droughts during the 2000s, as is the NEAF region; however, the 2010s represent a major drought period for NEAF (Figure S2 and S3). Finally, in the SEAF and SAF regions, the 1990s and 2020 s stand out as the most prominent drought periods (Figure S4 and S5).

The analysis of the characteristics of the main drought periods identified through SPI and SPEI indices shows an increasing duration, severity, and intensity with the scale of the indices (Figure 5 and 6). The most severe drought episodes identified through the SPI indices exhibit marked differences depending on the time scale. For SPI-3, the driest event lasted 20 months, with a severity of -7.15 units and an intensity of -0.36 units (Figure 5a, d and g). SPI-6 indicates a prolonged drought of 48 months, associated with a severity of -15.26 units and an intensity of -0.32 units (Figure 5b, e and h). The SPI-12 index captures the longest and most severe episode, spanning 72 months with a severity of -27.40 units and an intensity of -0.38 units (Figure 5c, f and i). Similarly, the SPEI indices reveal severe drought events. SPEI-3 records a duration of 30 months, a severity of -15.37 units, and an intensity of -0.51 units (Figure 6a, d and g). SPEI-6 shows a slightly shorter duration of 27 months, with a severity of -14.75 units and a stronger intensity of -0.55 units (Figure 6b, e and h). Finally, SPEI-12 highlights a long-lasting drought of 71 months, with a severity of -27.38 units and an intensity of -0.39 units (Figure 6c, f and i). This progression in drought characteristics reflects a gradual accumulation of water deficits as the time scale increases, which is particularly relevant for monitoring hydrological and agricultural droughts over semi-arid regions of Africa (Gebremichael et al., 2024; Ogunrinde et al., 2025).

The comparative analysis of time series derived from the SPI and SPEI indices reveals distinct trends in drought characteristics over recent decades (Figure 5 and 6). For the SPI index, the decades 1985–1994 and 1995–2004 are marked by a notable increase in drought duration, severity, and intensity across all considered time scales (SPI-3, SPI-6, and SPI-12) (Figure 5). This upward phase is followed,

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Figure showing the chronicle of the SPI and SPEI with the detection of the driest period: duration, severity and intensity from 1979 to 2024 over semi-arid Africa. Major drought event is highlighted in yellow.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Fig. 5. Evolution curves of drought duration, severity, and intensity, along with their trends, for SPI-3 (a, d, and g), SPI-6 (b, e, and h), and SPI-12 (c, f, and i).</figcaption></figure>

during the decades 2005–2014 and 2015–2024, by an opposite trend, reflected in a simultaneous decrease in duration, severity, and intensity (Figure 5). Regarding the SPEI index, the temporal dynamics partially diverge from those of the SPI. The periods 1979–1984, 1985–1994, and 2015–2024 exhibit a significant upward trend in drought duration, severity, and intensity (Figure 6). In contrast, the decades 1995–2004 and 2005–2014 are characterized by a decreasing trend in drought duration, severity and intensity across SPEI-3, SPEI-6 and SPEI-12 (Figure 6). The downward trend observed between 1995 and 2014 is linked to a strong decrease in potential evapotranspiration during the period 1998–2013 (Yang et al., 2019).

The highest drought intensities are observed for SPEI at the 3- and 6-month time scales, reflecting its heightened sensitivity to atmospheric evaporative demand. Unlike SPI, the Standardized Precipitation Evapotranspiration Index (SPEI) accounts for both precipitation and potential evapotranspiration, allowing it to better capture short-term droughts that are not solely driven by rainfall deficits (Tomas-Burguera et al., 2020). In warm climates, elevated temperatures substantially increase evaporative demand, accelerating the loss of soil moisture and intensifying drought stress, even when precipitation levels remain relatively stable. Several studies have shown that SPEI-3 and SPEI-6 are particularly responsive to fluctuations in evapotranspiration and exhibit stronger correlations with soil moisture and vegetation dynamics in semi-arid environments (Stefanidis et al., 2023; Törnros and Menzel, 2014). Consequently, the elevated intensity values recorded for SPEI-3 and SPEI-6 indicate intensified short-term drought episodes exacerbated by rising evaporative demand.

## 3.2. Regional analysis

### 3.2.1. Mediterranean region

The analysis of the p-value histograms for drought duration indicates that only a small fraction of pixels in the Mediterranean (MED) region exhibits statistically significant trends at the 0.05 level for the SPI indices (SPI-3, SPI-6, and SPI-12), with proportions ranging from 7 % to 14 % across the region (Fig. 7a, d, and g). Among these, at least 70 % of the significant pixels show decreasing trends, primarily located in the western part of MED, with drought durations declining by up to 2 months per decade (Fig. 8a-c). In contrast, when evapotranspiration is taken into account, as is the case with the SPEI indices, the proportion of significant pixels increases markedly, reaching approximately 62 % of the region (Fig. 7j, m, and p). Of these, at least 94 % show increasing trends in drought duration (Fig. 8j, m, and p). This pattern is especially pronounced in the western Mediterranean (between 18°W and 10°E), where drought durations increase by 1.5–3 months per decade (Fig. 8d-f).

Similarly, the histogram for drought severity shows limited significant trends for SPI, with only 9–11 % of pixels affected (Fig. 7b, e, h). Most of these pixels (at least 60 %) show decreasing severity, with values dropping by 0.5–1.5 units per decade (Fig. 9a-c). In
<figure><img src="image_8.png" /><figcaption>Fig. 6. Evolution curves of drought duration, severity, and intensity, along with their trends, for SPEI-3 (a, d, and g), SPEI-6 (b, e, and h), and SPEI-12 (c, f, and i).</figcaption></figure>

contrast, SPEI-based severity analysis reveals that 72 % of pixels display statistically significant trends (Fig. 7k, n, q), with 95 % of them showing increasing severity (Fig. 7k, n, q), ranging from 0.5 to 3 units per decade (Fig. 9d–f). The western Mediterranean region (18°W to 18°E) is especially affected, with a widespread and notable increase in drought severity. For drought intensity, the proportion of significant pixels is comparable to that observed for duration and severity, ranging from 9 % to 24 % for the SPI indices (Fig. 7c, f, i) and from 70 % to 75 % for the SPEI indices (Fig. 7l, o, r). Among these significant trends, more than 92 % indicate an increase in intensity (Fig. 7f, i, l, o, r). On average, drought intensity increases by approximately 0.5 units per decade for both SPI and SPEI across the region (Fig. 10).

### 3.3. Sahel and NEAF region

In the Sahel and NEAF regions, the analysis of p-value histograms for drought duration indicates that only a small portion of pixels show statistically significant trends at the 0.05 level for SPI (SPI-3, SPI-6, SPI-12), with values ranging from 20 % to 27 % (Fig. 11a, d, g). In the eastern part of the region (20°E to 42°E), drought duration shows an increasing trend of up to 2 months per decade, while in the western Sahel (20°W to 20°E), decreasing trends of 1–2 months per decade are observed (Fig. 12a–c). When evapotranspiration is considered, as in the SPEI indices, the percentage of significant pixels remains similar (24–27 %) (Fig. 11j, m, p). However, almost the entire NEAF region (32°W to 46°E) shows increasing drought duration trends, reaching 2–3 months per decade (Fig. 12d–f). Among these significant pixels, at least 39 % exhibit positive trends (Fig. 11j, m, p). A localized decreasing trend of 2–3 months per decade is also noted in some areas (Fig. 12d–f).

As with drought duration, the p-value histogram analysis for drought severity in the Sahel and NEAF regions shows that only a limited proportion of pixels exhibit statistically significant trends at the 0.05 level for the SPI indices (SPI-3, SPI-6, SPI-12), ranging from 21 % to 28 % (Fig. 11b, e, h). Most of these significant trends indicate decreasing severity, with reductions of 0.5–2 units per decade (Fig. 13a–c). For SPEI-based severity, 26–29 % of the pixels show significant trends (Fig. 11k, n, q), and among them, 37–59 % exhibit increasing severity, with values rising by 1–3 units per decade (Fig. 13d–f). A strong increase is particularly evident across most of the NEAF region (36°W to 46°E). Regarding drought intensity, similar proportions of significant pixels are observed: between 14 % and 25 % for SPI (Fig. 11c, f, i), and between 19 % and 24 % for SPEI (Fig. 11l, o, r). Among these, 32–78 % show increasing trends. The overall rise in intensity is estimated between 0.25 and 0.5 units per decade for both indices (Fig. 14).
Histograms of p-values and Sen Slope in MED

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Histogram of p-value of Mann Kendall test and the percentage of values of the Theil-Sen slope for the characteristics of SPI and SPEI in Semi-arid of MED.</figcaption></figure>

### 3.4. SAF and SEAF region

In the SAF and SEAF regions, the analysis of p-value histograms for drought duration shows that between 32 % and 35 % of pixels exhibit statistically significant trends for the SPI indices (SPI-3, SPI-6, SPI-12) (Fig. 15a, d, g). Among these, at least 70 % display increasing trends, primarily located in the northern and central parts of the region (Fig. 16a-c). These trends indicate an increase in drought duration ranging from 1 to 3 months per decade. In contrast, most of the significant decreasing trends in drought duration are found in SEAF, with reductions ranging from 1 to 3 months per decade (Fig. 16a-c). For the SPEI indices, approximately 30 % of pixels across SAF and SEAF show statistically significant trends (Fig. 15j, m, p). Of these, 64–66.7 % reflect increasing drought duration, with values ranging from 2 to 3 months per decade (Fig. 16d–f). The majority of positive trends are concentrated in the southern part of SAF, where drought duration is increasing by 1–3 months per decade (Fig. 16d–f).

The percentage of pixels with significant trends in drought severity for SPI indices is between 30 % and 38 % (Fig. 15b, e, h). For the SPI indices, increasing severity trends are mainly concentrated in the northern part of the SAF region, with values ranging from 1 to 2.5 units per decade (Fig. 17a-c). In contrast, most of the significant decreasing trends are located in the SEAF region, where severity declines by 1–2 units per decade, similarly to the spatial pattern observed for drought duration. For the SPEI indices, severity trends
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Map showing the trend (Theil-Sen slope) in the duration of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in MED.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Map showing the trend (Theil-Sen slope) in the severity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in MED.</figcaption></figure>

show a different spatial distribution. The percentage of pixels with a significant trend in drought severity for SPEI ranges from 25 % to 35 %. Increasing trends are more prominent in the southern part of the region, where at least 69 % of significant pixels exhibit positive trends (Fig. 15n, q), with severity increasing by 1.5–3 units per decade (Fig. 17d–f). Conversely, decreasing trends are observed in SAF and SEAF, with reductions of up to 2 units per decade. Interestingly, the spatial distribution of significant positive trends in severity appears to follow opposite patterns for SPI and SPEI around latitude 20°S (Fig. 17).

The p-value histograms for drought intensity reveal fewer significant pixels compared to drought severity: between 14 % and 17 % for the SPI (Fig. 15c, f, i), and between 19 % and 35 % for the SPEI (Fig. 15l, o, r). Among these, more than 78 % of the significant trends are positive for both SPI and SPEI (Fig. 15l, o, r), indicating a general intensification of drought conditions. Spatially, increasing trends in drought intensity are observed across the SEAF region and throughout the southern part of SAF, particularly south of 20°S (Fig. 18d–f). The magnitude of these increases ranges from 0.25 to 0.5 units per decade for both indices.

### 3.5. Madagascar region

In the MDG region, the analysis of drought duration trends shows that 31–37 % of pixels exhibit statistically significant changes for the SPI indices (SPI-3, SPI-6, SPI-12) (Fig. 19a, d, g). Most of these trends are positive, with drought duration increasing by 1–2 months per decade, particularly across western Madagascar, except in the extreme south, where no significant trend is observed (Fig. 20a–c).
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Map showing the trend (Theil-Sen slope) in the intensity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in MED.</figcaption></figure>

For the SPEI indices (SPEI-3, SPEI-6, SPEI-12), the proportion of significant pixels is slightly higher, ranging from 31 % to 40 % (Fig. 19j, m, p). Among these, 75 % show increasing trends (Fig. 19j, m, p), with drought duration increasing by 1–3 months per decade. However, a localized decrease of about 1 month per decade is observed in the far southern part of the island (Fig. 20d–f).

The histogram of drought severity trends for SPI indices (SPI-3, SPI-6, SPI-12) shows that 35–40 % of pixels display statistically significant trends (Fig. 19b, e, h). The spatial distribution of these trends is similar to that of drought duration, with increasing severity observed mainly in the semi-arid western and central regions, while the extreme south shows little or no significant change (Fig. 21a–c). The magnitude of severity increases for SPI ranges from 1 to 2 units per decade. For SPEI, the percentage of pixels with significant severity trends is even higher, ranging from 40 % to 55 % (Fig. 19k, n, q). Among these, at least 67 % show positive trends, with severity increases between 1 and 4 units per decade (Fig. 21d–f), indicating a stronger response of SPEI to the combined effects of precipitation deficits and rising evaporative demand.

Regarding drought intensity, the proportion of significant pixels is lower than that observed for severity. For the SPI indices, between 10 % and 28 % of pixels show statistically significant trends (Fig. 19c, f, i), while for SPEI, the proportion ranges from 16 % to 28 % (Fig. 19l, o, r). Among these, 33–60 % of SPI trends and 75–100 % of SPEI trends are positive, reflecting a more consistent intensification captured by SPEI (Fig. 19l, o, r). On average, drought intensity increases by approximately 0.5 units per decade for both indices. However, in some areas, particularly at the 6- and 12-month timescales, a comparable decrease in intensity is also observed, indicating spatial and temporal variability in drought dynamics (Fig. 22).

# 4. Discussion

## 4.1. Continental scale findings

The continent-wide assessment reveals three critical insights into drought dynamics across semi-arid Africa. First, temperature-driven evaporative demand, captured by SPEI, has emerged as a dominant driver of drought intensification in several regions, particularly where SPI trends remain weak or ambiguous. Second, the spatial heterogeneity of drought trends underscores the inadequacy of continent-scale generalizations, with neighboring regions exhibiting contrasting trajectories. Third, the divergence between precipitation-based (SPI) and climate-based (SPEI) indices emphasizes the necessity of multi-index approaches for comprehensive drought characterization under warming conditions.

Our continent-wide aggregated analysis of semi-arid Africa indicates three major drought episodes consistently emerge across all timescales and indices: the early 1990s, early 2000s, and the recent onset beginning in 2022. These findings align with documented continental-scale drought events reported in previous studies (Masih et al., 2014; Tall et al., 2023), confirming the robustness of our identification methodology.

While SPI shows increasing drought characteristics during 1985–2004 followed by improvement during 2005–2024, SPEI reveals intensification during 1979–1984, 1985–1994, and crucially, 2015–2024. This divergence reflects the fundamental difference between precipitation-driven and evaporative demand-driven drought processes (Vicente-Serrano et al., 2010; Wang et al., 2012). The SPI improvement post-2005 corresponds to documented rainfall recovery in parts of Africa, particularly the Sahel (Nicholson, 2013; Panthou et al., 2018). However, SPEI's sustained intensification during 2015–2024 reveals that temperature-driven increases in atmospheric water demand are offsetting, and in some regions overwhelming precipitation gains.

This pattern aligns with the "evaporative paradox" documented in semi-arid regions globally, where concurrent increases in
<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Histogram of the p-value of the Mann Kendall test and the percentage of values of the Theil-Sen slope for the characteristics of the SPI and SPEI in Semi-arid of the Sahel and NEAF.</figcaption></figure>

precipitation and temperature produce net aridification through enhanced evaporative demand (McVicar et al., 2012; Milly and Dunne, 2016). The finding that SPEI-3 and SPEI-6 show the highest drought intensities (-0.51 and -0.55 units respectively) compared to longer timescales underscores the heightened sensitivity of short-term moisture dynamics to temperature fluctuations. These short-term indices are particularly relevant for agricultural drought, as crop water stress responds rapidly to evaporative demand during critical growth stages (Tomas-Burguera et al., 2020; Liu et al., 2020).

The 2022 drought onset and implications for recent climate change. The clear identification of drought intensification beginning in 2022, particularly evident in SPEI-3 and SPEI-6, represents a significant finding with immediate policy relevance. This recent event occurs against a backdrop of accelerating continental warming, with African land areas warming approximately 0.3°C per decade since 1980—faster than the global average (Engelbrecht et al., 2015). The prominence of this event in SPEI but not SPI suggests it is fundamentally a "hot drought" (Overpeck, 2013), driven more by evaporative demand than precipitation deficit.

The systematic differences between SPI and SPEI evolution demonstrate that reliance on any single drought index can produce incomplete or misleading assessments. SPI, while valuable for characterizing precipitation anomalies, fundamentally cannot capture temperature-driven intensification of water deficits. This limitation has profound implications for drought early warning systems,
<figure><img src="image_14.png" /><figcaption>Fig. 12. Map showing the trend (Theil-Sen slope) in the duration of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in Sahel and NEAF. The red frame represents the delimitation of the Sahel; the dashed lines indicate the boundaries of the western, central, and eastern Sahel subregions; the black frame delineates the NEAF region.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 13. Map showing the trend (Theil-Sen slope) in the severity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in Sahel and NEAF. The red frame represents the delimitation of the Sahel; the dashed lines indicate the boundaries of the western, central, and eastern Sahel subregions; the black frame delineates the NEAF region.</figcaption></figure>

many of which still rely primarily on precipitation-based indices (Sheffield et al., 2012; Hao et al., 2018). Our results support the growing consensus that operational drought monitoring must incorporate temperature and evaporative demand, particularly in regions experiencing rapid warming (AghaKouchak et al., 2015; Mukherjee et al., 2018).

The continental-scale aggregation employed here, averaging across six major semi-arid regions, provides a synthetic overview of drought evolution but necessarily masks important spatial heterogeneity. As demonstrated in subsequent regional analyses (Sections 3.2 and 4.2), the uniform continental-scale drought episodes identified here emerge from spatially diverse and sometimes opposing regional trends. This underscores the limitation of continental-scale generalizations and the necessity of region-specific drought assessments for adaptation planning (Haile et al., 2020; Spinoni et al., 2020).

## 4.2. Regional interpretation in the context of the literature

### 4.2.1. Mediterranean region

The Mediterranean region exemplifies the transition from precipitation-limited to temperature-limited drought regimes. Our finding that only 7–25 % of pixels show significant SPI trends, while up to 75 % exhibit significant SPEI trends, quantifies this transition with unprecedented spatial detail. This divergence fundamentally challenges drought monitoring systems that rely solely on
<figure><img src="image_16.png" /><figcaption>Fig. 14. Map showing the trend (Theil-Sen slope) in the intensity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in Sahel and NEAF. The red frame represents the delimitation of the Sahel; the dashed lines indicate the boundaries of the western, central, and eastern Sahel subregions; the black frame delineates the NEAF region.</figcaption></figure>

precipitation metrics. The low percentage of significant pixels for drought duration, severity, and intensity observed in the SPI indices can be attributed to the limited significance of precipitation trends in the Mediterranean region. Given the 95 % confidence level adopted in the Mann–Kendall test, a proportion of significant pixels close to 5 % is statistically expected by chance alone, meaning that such sparse signals likely do not indicate true regional trends but rather random variability. Precipitation in this area is strongly influenced by natural atmospheric circulation patterns such as the North Atlantic Oscillation (NAO) and the Mediterranean Oscillation (MO), which introduce substantial interannual variability and mask long-term trends (Boughdadi et al., 2023; Djetbar et al., 2020; Tramblay et al., 2018; Vicente-Serrano et al., 2025). In contrast, the increasing aridity in the region is driven less by declining precipitation than by rising temperatures, which lead to higher atmospheric evaporative demand (Afamondji et al., 2025; Tramblay et al., 2018; Vicente-Serrano et al., 2025). The analysis of temperature trends reveals a statistically significant warming across the entire MED region at the 0.05 significance level (Figure S7). Maximum temperature exhibits the strongest increase, reaching up to 1.5 °C per decade, while both mean and minimum temperatures show an increase of approximately 1 °C per decade (Figure S7). This enhanced evaporative demand has contributed more significantly to the intensification of agricultural and ecological droughts than to meteorological droughts, particularly in North Africa (Tanarhte et al., 2024). This explains the much higher percentage of significant positive trends observed in SPEI-based indicators of drought duration, severity, and intensity. Moreover, the SPEI drought index, particularly SPEI-12, has been identified as the primary factor controlling fluctuations in total water storage (TWS) in the Mediterranean region (Afamondji et al., 2025). TWS exhibits a strong declining trend in this region (Afamondji et al., 2025). This explains the high proportion of increasing trends in drought duration, severity, and intensity for SPEI (SPEI-3, SPEI-6, and SPEI-12) in the Mediterranean region.

## 4.3. Sahel and NEAF

The spatial and temporal variability of drought trends across the Sahel and Northeast Africa (NEAF) reveals a complex picture consistent with, yet extending, current understanding of climate impacts in semiarid Africa. Statistically significant trends occur in only 20–29 % of pixels for both SPI and SPEI, indicating that drought changes remain spatially heterogeneous rather than regionally uniform.

## 4.4. Precipitation recovery and zonal contrast in the sahel

Our results show decreasing drought duration (1–2 months per decade) in the western Sahel (20°W–20°E) and increasing duration (up to 2 months per decade) in the eastern sector (20°E–42°E). This west–east gradient supports recent evidence of contrasting rainfall evolution, with projected declines in the western Sahel and recovery in central areas (Monerie et al., 2020a, 2023; Nicholson et al., 2018). SPI-based severity decreases coincide with zones of rainfall recovery, consistent with observed increases in rainfall variability and intensity since the 1980s (Chen et al., 2002; Xie et al., 2007). However, the limited number of significant trends suggests that recovery remains partial, echoing findings by Sheffield et al. (2012) of modest global drought change. Orographic and latitudinal influences likely modulate these spatial contrasts (Diatta et al., 2020).
Histograms of p-values and Sen Slope in SAF and SEAF

<figure><img src="image_17.png" /><figcaption>Fig. 15. Histogram of the p-value of the Mann Kendall test and the percentage of values of the Theil-Sen slope for the characteristics of the SPI and SPEI in Semi-arid of SAF and SEAF.</figcaption></figure>

4.5. *Temperature-driven drought intensification*

A notable divergence between SPI and SPEI trends, particularly over NEAF, highlights the critical role of temperature-driven evapotranspiration. SPEI indicates widespread increases in drought duration (2–3 months per decade) and severity (1–3 units per decade), even where SPI shows minimal change. This underscores the importance of accounting for evaporative demand in drought assessment (Vicente-Serrano et al., 2010). Rising temperatures across the region (Fan and van den Dool, 2008; Vose et al., 2012) appear to exacerbate drought stress, explaining the coexistence of precipitation recovery and persistent aridity. Consistent warming trends and increased heat extremes documented by Gebrechorkos et al. (2019a), (2019b) and Wubaye et al. (2023) reinforce the dominant influence of temperature on regional drought dynamics.

4.6. *Spatial heterogeneity and data considerations*

The observed spatial heterogeneity aligns with findings from high-resolution precipitation products such as TAMSAT and CMORPH
<figure><img src="image_18.png" /><figcaption>Fig. 16. Map showing the trend (Theil-Sen slope) in the duration of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in SAF and SEAF. The red frame represents the delimitation of SAF; the dashed lines indicate the boundaries between the northern SAF region (above 20°S) and the southern SAF region (below 20°S); the black frame delineates the SEAF region.</figcaption></figure>

(Maidment et al., 2014; Joyce et al., 2004). The CPC dataset used here offers an optimal balance between spatial coverage and daily resolution (Chen et al., 2002), though sparse gauge networks and high interannual variability limit signal detection. Comparison with alternative datasets such as GPCP (Xie and Arkin, 1997) or GHCN (Lawrimore et al., 2011) could strengthen this analysis.

## 4.7. Implications for drought monitoring

The divergence between SPI and SPEI has direct implications for drought monitoring. While SPI remains useful in rainfall-dominated systems, SPEI provides a more realistic representation of agricultural and hydrological drought under warming conditions (Sheffield et al., 2012; Ziese et al., 2014). The strong increase in SPEI-based severity across NEAF indicates heightened vulnerability in temperature-sensitive areas such as Sudan, Ethiopia, and the Horn of Africa, where drought-driven food insecurity remains critical (Uhe et al., 2018; Makokha et al., 2024). Increasing drought intensity, even without higher frequency, suggests that future early warning systems must emphasize drought severity and compound risks under climate change.

## 4.8. SAF and SAEF

The results in SAF and SAEF show significant increasing trends in drought duration, severity, and intensity based on SPI indices north of 20°S in the western part of SAF, where precipitation is declining. This drying trend is consistent with long-term observations and projections showing reduced rainfall across the region (Carvalho et al., 2017; Chisanga et al., 2023). The dominance of high-pressure systems and east-to-west gradients in atmospheric circulation contribute to reduced moisture inflow (Spinage, 2012). In this zone, droughts are primarily driven by rainfall variability, influenced by low-level jet streams like the Zambezi jet (Pereira et al.,
<figure><img src="image_19.png" /><figcaption>Fig. 17. Map showing the trend (Theil-Sen slope) in the severity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in SAF and SEAF. The red frame represents the delimitation of SAF; the dashed lines indicate the boundaries between the northern SAF region (above 20°S) and the southern SAF region (below 20°S); the black frame delineates the SEAF region.</figcaption></figure>

2024) and large-scale climate modes such as ENSO and the Indian Ocean Dipole (Likoya et al., 2023). Although temperatures are also increasing, SPEI trends remain limited in this area, likely because drought conditions are still more strongly linked to precipitation deficits than to evaporative demand. Moreover, the combined analysis of precipitation and temperature trends clearly indicates that the region north of 20°S is characterized by a significant decline in precipitation, reaching up to 90 mm per decade, accompanied by a notable increase in temperature over the same period (Figure S8).

In contrast, in the southern part of SAF (south of 20°S), the results show strong positive trends in drought duration and severity based on SPEI, indicating the growing role of temperature-driven aridity. This region has experienced substantial warming, with temperature increases of up to 2°C, and a rise in heat extremes (Kruger and Nxumalo, 2017; Meyer et al., 2025; Nkemelang et al., 2018). Indeed, precipitation trends are not statistically significant in the region south of 20°S (Figure S8a). However, this part of southern Africa exhibits a pronounced warming, with temperature increases that are more substantial than those observed north of 20°S (Figure S8b and S8c). In particular, maximum temperatures rise by up to 2°C per decade, highlighting an intensifying warming signal in this region (Figure S8c). These climatic conditions increase atmospheric evaporative demand, which is well captured by SPEI. Moreover, this area also faces declining precipitation and reduced rainfall frequency (Adeola et al., 2022; Mengistu et al., 2020), further intensifying drought conditions. Together, these regional contrasts explain why SPI trends are more pronounced in the north, where drought is mainly precipitation-driven, while SPEI trends dominate in the south, where temperature and evaporative demand are the main contributors. These contrasting drought dynamics highlight the need to use both SPI and SPEI to fully capture the regional diversity of drought drivers in SAF. The latitudinal divide at approximately 20°S represents not merely a statistical artifact but reflects fundamental differences in atmospheric circulation controls. North of 20°S, drought is predominantly driven by rainfall deficits linked to the Angola Low and tropical temperate troughs (Reason and Jagadheesha, 2005), explaining the prominence of SPI trends. South of 20°S, the region falls increasingly under the influence of subtropical high-pressure systems where temperature effects dominate
<figure><img src="image_20.png" /><figcaption>**Fig. 18.** Map showing the trend (Theil-Sen slope) in the intensity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in SAF and SEAF. The red frame represents the delimitation of SAF; the dashed lines indicate the boundaries between the northern SAF region (above 20°S) and the southern SAF region (below 20°S); the black frame delineates the SEAF region.</figcaption></figure>

(Kruger and Sekele, 2013). The higher sensitivity of SPEI in this southern zone aligns with observed increases in the frequency and intensity of heatwaves (Engelbrecht et al., 2015), which amplify evaporative demand independent of precipitation changes.

For the SEAF region the results highlight spatial contrasts in drought trends. In the northern part of SEAF, significant increases in drought duration, severity, and intensity were observed, particularly in areas captured by both SPI and SPEI indices. These trends can be explained by the combined effects of La Niña conditions, which tend to reduce rainfall, and a consistent rise in temperatures, which intensifies atmospheric evaporative demand (Gebrechorkos et al., 2020, 2019b; Uhe et al., 2018). The cumulative impact of repeated droughts in this subregion supports the increasing trends identified in the temporal analysis. In the central part of SEAF, the trends are more mixed. Our results show decreasing drought duration and severity, especially for SPI, which may reflect improved rainfall conditions during the short rainy season, offsetting long-term drying trends. Meanwhile, in the eastern and coastal parts of SEAF, SPEI-based indicators show increasing drought trends, in line with observed reductions in rainfall along the coast. These patterns may be associated with changes in anticyclonic circulation over the southwest Indian Ocean, which influence moisture transport in the region (Borhara et al., 2020; Kabanda, 2018). The spatial variability of drought trends in SEAF reflects the interplay between regional rainfall variability, large-scale climate drivers (such as ENSO and the Indian Ocean Dipole), and temperature-driven increases in evaporative demand, particularly in the eastern and northern subregions (Afamondji et al., 2025).

## 4.9. Madagascar

Southern Madagascar is semi-arid with a short, highly seasonal rainy season (roughly October–March) and strong interannual variability tied to large-scale ocean–atmosphere modes over the Indian and Pacific Oceans. Recent humanitarian crises have coincided
Histograms of p-values and Sen Slope in MDG

<figure><img src="image_21.png" /><figcaption>**Fig. 19.** Histogram of the p-value of the Mann Kendall test and the percentage of values of the Theil-Sen slope for the characteristics of the SPI and SPEI in Semi-arid of MDG.</figcaption></figure>

with droughts, notably in 1990–1992 and again in 2019–2021, culminating in severe food insecurity. During the last four decades warming has been the strongest in the south and west of Madagascar, elevating evapotranspiration and preconditioning agricultural and ecological droughts (Randriamarolaza et al., 2022).

In Southern Madagascar, detection of long-term rainfall trends is hampered by sparse gauges. Nonetheless, satellite (CHIRPS) and model (ECMWF) rainfall estimates as well as vegetation indices point to more frequent and intense droughts since the early 1990s (Randriatsara et al., 2025). Climate projections suggest continued warming, delayed rainy seasons, and longer dry spells, even where late-season rainfall may increase (Barimalala et al., 2021).

Our results for southern Madagascar require careful contextualization given recent severe droughts documented in the region (Randriatsara et al., 2025; Rigden et al., 2024). Several methodological factors may explain the apparent difference between our results and recent humanitarian crises. First, the CPC dataset's reliance on sparse gauge networks in southern Madagascar (Chen et al., 2008) may inadequately capture localized extreme events. Second, our annual aggregation approach, while suitable for detecting long-term trends, may mask critical intra-seasonal dynamics. As Rigden et al. (2024) demonstrate, delayed rainy season onset—rather than reduced annual totals—has driven recent agricultural droughts, creating severe impacts despite statistically non-significant
<figure><img src="image_22.png" /><figcaption>Fig. 20. Map showing the trend (Theil-Sen slope) in the duration of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in MDG.</figcaption></figure>

annual trends.

This case illustrates a fundamental distinction in drought conceptualization: agronomic drought (determined by timing, intensity, and distribution within the growing season) versus meteorological drought (typically assessed over annual or multi-month periods). The human toll of Madagascar's recent droughts (Harrington et al., 2022) occurred despite relatively modest changes in annual precipitation, emphasizing that trend-based assessments must be complemented by event-based analyses that capture seasonal phasing and intensity.

## 4.10. Main limitations and future work

While the present analysis relies solely on CPC data for precipitation and temperature, future studies would benefit from comparing results across multiple climate datasets. Inter-dataset comparison can help quantify uncertainties related to observational methods, spatial resolution, and interpolation schemes, which often influence the estimation of drought indices and trend detection (Beck et al., 2017; Maidment et al., 2017; Mankin et al., 2025). Evaluating consistency among datasets can improve confidence in detected changes and highlight regions or seasons where observational uncertainty remains high. In particular, integrating products such as CRU (Climatic Research Unit Time Series) (Harris et al., 2020), GPCC (Global Precipitation Climatology Centre) (Schneider et al., 2018), ERA5 (Hersbach et al., 2020), and MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2; Gelaro et al., 2017) could provide complementary perspectives.”

Beyond the single-dataset limitation, several methodological choices shape our results and their interpretation. Our event-based approach to defining drought characteristics, while standard in the literature (Spinoni et al., 2014), introduces truncation at annual boundaries for multi-year events. A drought persisting across three consecutive years is represented as three separate annual events, potentially inflating duration frequencies while obscuring multi-year persistence. Alternative approaches, such as run theory applied to the full time series without annual subdivision (Yevjevich, 1967), might better capture long-term drought memory.

The Thornthwaite PET method, justified by data availability and computational efficiency, represents a significant simplification of the actual evaporative process. Comparison with Penman-Monteith-based SPEI would provide important uncertainty bounds
<figure><img src="image_23.png" /><figcaption>**Fig. 21.** Map showing the trend (Theil-Sen slope) in the severity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in MDG.</figcaption></figure>

(Vicente-Serrano et al., 2020).

## 5. Conclusion

This study offers a comprehensive and spatially explicit assessment of drought dynamics across Africa's semi-arid regions over the period 1979-2024. By analyzing drought characteristics, namely duration, severity, and intensity, through both SPI and SPEI indices at multiple time scales (3, 6, and 12 months), we demonstrate how the inclusion of evapotranspiration in SPEI reveals patterns and trends that precipitation-based indices (SPI) alone tend to underestimate or overlook.

One of the most compelling findings is the identification of a recent intensification of droughts beginning around 2022, clearly detected by SPEI-3 and SPEI-6, particularly in regions where rising temperatures are increasing evaporative demand. This highlights the growing dominance of temperature-driven aridity over traditional precipitation deficits, especially in the Mediterranean region, southern Africa, and Madagascar. In these regions, significant positive trends in drought severity, duration, and intensity are observed over large spatial extents, with values reaching up to 3 months per decade for duration, 3 units per decade for severity and 0.5 units per decade for intensity. These trends are markedly less pronounced in the Sahel and North-Eastern Africa, where drought variability remains more closely tied to fluctuations in rainfall rather than temperature.

Despite this, parts of North Eastern-Africa display intensifying drought characteristics captured by both SPI and SPEI, indicating the cumulative influence of declining precipitation and increasing atmospheric demand. In contrast, western Sahel exhibits localized reductions in drought severity and duration, suggesting divergent regional behavior. Similarly, in South Eastern-Africa, SPEI-based trends dominate along the coastal and northern sectors, while central areas show mixed or even decreasing trends in some drought metrics, reflecting seasonal rainfall recovery. In Madagascar, the western semi-arid areas show clear intensification of droughts, whereas the southern tip of the island exhibits more localized and complex changes. It is important to note that the MDG region suffers from a scarcity of gauged data, which may lead to biases in the estimated trends of drought characteristics, particularly in the southernmost part of Madagascar.

The comparative performance of SPI and SPEI across these diverse regions underscores the importance of integrating temperature
<figure><img src="image_24.png" /><figcaption>**Fig. 22.** Map showing the trend (Theil-Sen slope) in the intensity of annual dry periods for pixels for which the Mann Kendal test is significant at the 0.05 threshold (grey pixels are non-significant) in MDG.</figcaption></figure>

effects when monitoring and projecting drought risk. SPEI consistently yields a higher proportion of significant trends, reaffirming its suitability for capturing the compound effects of warming and rainfall variability across semi-arid zones.

While CPC datasets provide spatially consistent long-term records across Africa, their accuracy remains limited in areas with sparse ground observations such as the Sahel and Madagascar, which may affect the detection of local rainfall extremes. Moreover, differences between SPI and SPEI formulations can lead to variations in drought magnitude under warming conditions. Hence, the patterns reported here should be interpreted as large-scale tendencies rather than local estimates. Future work should compare multiple datasets, such as CRU, GPCC, ERA5, or MERRA-2, to better quantify uncertainties and improve regional reliability.

Finally, this study reveals that Africa's semi-arid regions are experiencing spatially heterogeneous but increasingly severe drought conditions. This intensification poses growing threats to water availability, agricultural productivity, and ecosystem sustainability. The high-resolution spatial mapping of these trends offers valuable guidance for identifying emerging hotspots of vulnerability, enabling targeted adaptation strategies and informing regional water management and food security policies under a changing climate

CRediT authorship contribution statement

**Ismail Mohsine:** Validation, Supervision, Software, Methodology, Investigation, Conceptualization. **Nourelhouda Karmouda:** Writing – review & editing, Writing – original draft, Visualization, Supervision, Methodology. **Ilias Kacimi:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Conceptualization. **Sarah Tweed:** Writing – review & editing, Writing – original draft, Project administration, Investigation, Funding acquisition, Conceptualization. **Tarik Bouramtane:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Methodology. **Mounia Tahiri:** Visualization, Validation, Supervision, Resources, Funding acquisition. **Komi Mawulom Claude Afamondji:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Methodology, Formal analysis, Data curation. **Marc Leblanc:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation. **Alyson Brayner Sousa Estacio:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Conceptualization.
Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.

Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ejrh.2025.103087.

Data availability

Data will be made available on request.

References

Adejuwon, J.O., Dada, E., 2021. Temporal analysis of drought characteristics in the tropical semi-arid zone of Nigeria. Sci. Afr. 14. https://doi.org/10.1016/j.sciaf.2021.e01016.

Adeola, A.M., Kruger, A., Makgoale, T.E., Botai, J.O., 2022. Observed trends and projections of temperature and precipitation in the Olifants River Catchment in South Africa. PLoS One 17. https://doi.org/10.1371/journal.pone.0271974.

Adisa, O.M., Masinde, M., Botai, J.O., Botai, C.M., 2020. Bibliometric analysis of methods and tools for drought monitoring and prediction in Africa. Sustain. https://doi.org/10.3390/sul12166516.

Afamondji, K.M.C., Mohsine, I., Karmouda, N., Bouramtane, T., Tahiri, M., Kacimi, I., Tweed, S., Driouech, F., Leblanc, M., 2025. Climate Extreme Indices Influencing GRACE Total Water Storage (TWS) in Semi-Arid Africa. J. Arid Environ. 229, 105357. https://doi.org/10.1016/j.jaridenv.2025.105357.

AghaKouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D., Hain, C.R., 2015. Remote sensing of drought: Progress, challenges and opportunities. Rev. Geophys. https://doi.org/10.1002/2014RG000456.

Akinyemi, F.O., 2021. Vegetation trends, drought severity and land use-land cover change during the growing season in semi-arid contexts. Remote Sens 13. https://doi.org/10.3390/rs13050836.

Al-Sudani, H.I.Z., 2019. Derivation mathematical equations for future calculation of potential evapotranspiration in Iraq, a review of application of thornthwaite evapotranspiration. Iraqi J. Sci. https://doi.org/10.24996/ijss.2019.60.5.13.

Amazir, A., Chehbouni, A., Bouras, E.H., Benkirane, M., Ait Hssaine, B., Entekhabi, D., 2023. Drought cascade lag time estimation across Africa based on remote sensing of hydrological cycle components. Adv. Water Resour. 182. https://doi.org/10.1016/j.advwatres.2023.104586.

Barimalala, R., Raholijao, N., Pokam, W., Reason, C.J.C., 2021. Potential impacts of 1.5 °C, 2 °C global warming levels on temperature and rainfall over Madagascar. Environ. Res. Lett. 16. https://doi.org/10.1088/1748-9326/abeb34.

Beck, H.E., Van Dijk, A.I., Levizzani, V., Schellekens, J., Miralles, D.G., Martens, B., De Roo, A., 2017. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci. 21 (1), 589–615.

Beguería, S., Vicente-Serrano, S.M., Reig, F., Latorre, B., 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Clim. 34. https://doi.org/10.1002/joc.3887.

Borhara, K., Pokharel, B., Bean, B., Deng, L., Wang, S.Y.S., 2020. On Tanzania's precipitation climatology, variability, and future projection. Climate 8. https://doi.org/10.3390/cli8020034.

Boughdadi, S., Ait Brahim, Y., El Alaoui El Fels, A., Saidi, M.E., 2023. Rainfall Variability and Teleconnections with Large-Scale Atmospheric Circulation Patterns in West-Central Morocco. Atmosphere 14. https://doi.org/10.3390/atmos14081293.

Boushaki, F.I., Hsu, K.L., Sorooshian, S., Park, G.H., Mahani, S., Shi, W., 2009. Bias adjustment of satellite precipitation estimation using ground-based measurement: a case study evaluation over the southwestern United States. J. Hydrometeorol. 10. https://doi.org/10.1175/2009JHM10991.1.

Brodribb, T.J., Powers, J., Cochard, H., Choat, B., 2020. Hanging by a thread? Forests and drought. Science 80. https://doi.org/10.1126/science.aat7631.

Carvalho, L.M.V., Jones, C., Posadas, A.N.D., Quiroz, R., Bookhagen, B., Liebmann, B., 2012. Precipitation characteristics of the South American monsoon system derived from multiple datasets. J. Clim. 25. https://doi.org/10.1175/JCLI-D-11-00335.1.

Carvalho, S.C.P., Santos, F.D., Pulquerio, M., 2017. Climate change scenarios for Angola: an analysis of precipitation and temperature projections using four RCMs. Int. J. Clim. 37. https://doi.org/10.1002/joc.4925.

Cavus, Y., Aksoy, H., 2020. Critical drought severity/intensity-duration-frequency curves based on precipitation deficit. J. Hydrol. 584. https://doi.org/10.1016/j.jhydrol.2019.124312.

Chen, C.T., Knutson, T., 2008. On the verification and comparison of extreme rainfall indices from climate models. J. Clim. 21. https://doi.org/10.1175/2007JCLI1494.1.

Chen, M., Shi, W., Xie, P., Silva, V.B.S., Kousky, V.E., Higgins, R.W., Janowiak, J.E., 2008. Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res. Atmos. 113. https://doi.org/10.1029/2007JD009132.

Chen, M., Xie, P., Janowiak, J.E., 2002. Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeorol. 3. https://doi.org/10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.

Chisanga, C.B., Nkonde, E., Phiri, E., Mubanga, K.H., Lwando, C., 2023. Trend analysis of rainfall from 1981-2022 over Zambia. Heliyon 9. https://doi.org/10.1016/j.heliyon.2023.e22345.

Chivangulula, F.M., Amraoui, M., Pereira, M.G., 2023. The Drought Regime in Southern Africa: a systematic review. Climate. https://doi.org/10.3390/cli11070147.

Chivangulula, F.M., Amraoui, M., Pereira, M.G., 2025. The Drought Regime in Southern Africa and Recent Climate Change: Long-Term Trends in Climate Elements, Drought Indices and Descriptors. Water 17 (21), 3031.

Cook, B.I., Anchukaitis, K.J., Touchan, R., Meko, D.M., Cook, E.R., 2016. Spatiotemporal drought variability in the mediterranean over the last 900 years. J. Geophys. Res. 121. https://doi.org/10.1002/2015JD023929.

Demissie, T., Sustainability, S.G., 2024. undefined, 2024. Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981-2021), 2024<mdpi.com> mdpi.comT Demissie SH GebrechorkosSustainability. https://doi.org/10.3390/sul1030885.

Diatta, S., Diedhiou, C.W., Dionne, D.M., Sambou, S., 2020. Spatial variation and trend of extreme precipitation in west africa and teleconnections with remote indices. Atmosphere (Basel) 11. https://doi.org/10.3390/atmos11090999.

Ding, Y., He, X., Zhou, Z., Hu, J., Cai, H., Wang, X., Lusheng, L., Jiatun, X, Shi, H., 2022. Response of vegetation to drought and yield monitoring based on NDVI and SIF. Catena 219, 106328.

Dinku, T., Ceccato, P., Connor, S.J., 2011. Challenges of satellite rainfall estimation over mountainous and arid parts of east Africa. Int. J. Remote Sens 32. https://doi.org/10.1080/01431161.2010.499381.
Dinku, T., Ceccato, P., Grover-Kopec, E., Lemma, M., Connor, S.J., Ropelewski, C.F., 2007. Validation of satellite rainfall products over East Africa's complex topography. Int. J. Remote Sens. 28. https://doi.org/10.1080/01431160060954688.

Dinku, T., Connor, S.J., Ceccato, P., Ropelewski, C.F., 2008. Comparison of global gridded precipitation products over a mountainous region of Africa. Int. J. Clim. 28. https://doi.org/10.1002/joc.1669.

Djebarb, A., Goose, H., Klein, F., 2020. Robustness of the link between precipitation in North Africa and standard modes of atmospheric variability during the last Millennium, 8, 62 Clim 2020 8, 62. https://doi.org/10.3390/CLI8050062.

Djomou, Z.Y., Monkam, D., Lenouo, A., 2009. Spatial variability of rainfall regions in West Africa during the 20th century. Atmos. Sci. Lett. 10. https://doi.org/10.1002/asl.202.

Engelbrecht, F., Adegoke, J., Bopape, M.-J., Naidoo, M., Garland, R., Thatcher, M., McGregor, J., Katzley, J., Werner, M., Ichoku, C., Gatebe, C., 2015. Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett. 10 (8), 085004.

Fan, Y., van den Dool, H., 2008. A global monthly land surface air temperature analysis for 1948-present. J. Geophys. Res. Atmos. 113. https://doi.org/10.1029/2007JD008470.

Filho, J.D.P., de Assis Souza Filho, F., Martins, E.S.P.R., de Carvalho Studart, T.M., 2020. Copula-based multivariate frequency analysis of the 2012-2018 drought in Northeast Brazil. Water (Switz.) 12. https://doi.org/10.3390/w12030834.

Filipović, L., Putniković, S., Stosic, B., Stosic, T., Djurjević, V., Tošić, I., 2025. Analysis of spatio-temporal characteristics of drought in Serbia From 1961 to 2020 Using SPI and SPEI. Int. J. Clim. 45, e8803. https://doi.org/10.1002/JOC.8803.

Gebrechorkos, S.H., Hülsmann, S., Bernhofer, C., 2019a. Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania. Int. J. Clim. 39. https://doi.org/10.1002/joc.5777.

Gebrechorkos, S.H., Hülsmann, S., Bernhofer, C., 2019b. Long-term trends in rainfall and temperature using high-resolution climate datasets in East Africa. Sci. Rep. 9. https://doi.org/10.1038/s41598-019-47933-8.

Gebrechorkos, S.H., Hülsmann, S., Bernhofer, C., 2020. Analysis of climate variability and droughts in East Africa using high-resolution climate data products. Glob. Planet. Change 186. https://doi.org/10.1088/gloplat2020103130.

Gebrechorkos, S.H., Peng, J., Dyer, E., Miralles, D.G., Vicente-Serrano, S.M., Funk, C., Beck, H.E., Asfaw, D.T., Singer, M.B., Dadson, S.J., 2023. Global high-resolution drought indices for 1981-2022. Earth Syst. Sci. Data 15. https://doi.org/10.5194/essd-15-5449-2023.

Gebremichael, H.B., Raba, G.A., Beketie, K.T., Feyisa, G.L., 2024. Temporal and spatial characteristics of drought, future changes and possible drivers over Upper Awash Basin, Ethiopia, using SPI and SPEI. Environ. Dev. Sustain. 26. https://doi.org/10.1007/s10668-022-02743-3.

Gelaro, R., McCarty, W., Suárez, M.J., Toddling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A.M., Gu, W., Kim, G.K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.E., Partyka, G., Pawson, S., Putman, W., Rie necker, M., Schubert, S.D., Sienkiewicz, M., Zhao, B., 2017. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30. https://doi.org/10.1175/JCLI-D-16-0758.1.

Guttman, N.B., 1999. Accepting the standardized precipitation index. A Calc. Algorithm J. Am. Water Resour. Assoc. 35. https://doi.org/10.1111/j.1752-1688.1999.tb03592.x.

Haile, G.G., Tang, Q., Hosseini-Moghari, S.M., Liu, X., Gebremicael, T.G., Leng, G., Kebede, A., Xu, X., Yun, X., 2020. Projected Impacts of Climate Change on Drought Patterns Over East Africa. Earth'S. Futur 8. https://doi.org/10.1029/2020EF001502.

Hao, Z., Singh, V.P., Xia, Y., 2018. Seasonal Drought Prediction: Advances, Challenges, and Future Prospects. Rev. Geophys 56. https://doi.org/10.1002/2016RG000549.

Harrington, L.J., Wolski, P., Pinto, I., Ramarosandratana, A.M., Barimalala, R., Vautard, R., Philip, S., Kew, S., Singh, R., Heinrich, D., Arrighi, J., Raju, E., Thalheimer, L., Razanakoto, T., van Aalst, M., Li, S., Bonnet, R., Yang, W., Otto, F.E.L., van Oldenborgh, G.J., 2022. Limited role of climate change in extreme low rainfall associated with southern Madagascar food insecurity, 2019–21. Environ. Res. Clim. 1. https://doi.org/10.1088/2752-5295/aca695.

Harris, I., Osborn, T.J., Jones, P., Lister, D., 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7. https://doi.org/10.1038/s41597-020-0453-3.

Hermance, J.F., Sulieman, H.M., 2014. Assessing daily and seasonal satellite rainfall estimates using local gauges for the anomalous 2012 monsoon season in the African East Sahel. Int. J. Remote Sens. 35. https://doi.org/10.1080/01431161.2013.866294.

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Holm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., Thépaut, J.N., 2020. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146. https://doi.org/10.1002/qj.3803.

Huang, J., Ji, M., Xie, Y., Wang, S., He, Y., Ran, J., 2016. Global semi-arid climate change over last 60 years. Clim. Dyn. 46. https://doi.org/10.1007/s00382-015-2636-8.

Huang, J., Zhang, C., Prospero, J.M., 2009. Large-scale effect of aerosols on precipitation in the West African Monsoon region. Q. J. R. Meteorol. Soc. 135. https://doi.org/10.1002/qj.391.

Janowiak, J.E., Joyce, R.J., Yarosh, Y., 2001. A real-time global half-hourly pixel-resolution infrared dataset and its applications. Bull. Am. Meteorol. Soc. 82. https://doi.org/10.1175/1520-0477(2001)082<0205>ARTGHH-2.3.CO;2.

Jiang, S. hu, Wei, L. yong, Ren, L. liang, Zhang, L. qi, Wang, M. hao, Cui, H., 2023. Evaluation of IMERG, TMPA, ERA5, and CPC precipitation products over mainland China: Spatiotemporal patterns and extremes. Water Sci. Eng. 16. https://doi.org/10.1016/j.wse.2022.05.001.

Joyce, R.J., Janowiak, J.E., Arkin, P.A., Xie, P., 2004. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeorol. 5. https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

Kabanda, T., 2018. Long-term rainfall trends over the Tanzania coast. Atmosphere 9. https://doi.org/10.3390/atmos9040155.

Karam, S., Zango, B.S., Seidou, O., Pereira, D., Nagabhatla, N., Tshimanga, R.M., 2023. Impacts of climate change on hydrological regimes in the Congo River Basin. Sustain 15. https://doi.org/10.3390/su15076066.

Kassaye, A.Y., Shao, G., Wang, X., Shifaw, E., Wu, S., 2021. Impact of climate change on the staple food crops yield in Ethiopia: implications for food security. Theor. Appl. Clim. 145. https://doi.org/10.1007/s00704-021-03635-8.

Kendall, M.G., 1975. Rank Correlation Methods, 4th edn. Charles Griffin, San Fr. CA.

Kogan, F., Guo, W., 2016. Early twenty-first-century droughts during the warmest climate. Geomat. Nat. Hazards Risk 7. https://doi.org/10.1080/19475705.2013.878399.

Kruger, A.C., Nxumalo, M., 2017. Surface temperature trends from homogenized time series in South Africa: 1931–2015. Int. J. Clim. 37. https://doi.org/10.1002/joc.4851.

Kruger, A.C., Sekele, S.S., 2013. Trends in extreme temperature indices in South Africa: 1962-2009. Int. J. Clim. 33. https://doi.org/10.1002/joc.3455.

Kumari, P., Shaik, R., Vannam, S.C., Singh, S.K., 2024. Drought evaluation using various evapotranspiration models over semi-arid river basins. J. Water Clim. Chang 15, 3582–3601. https://doi.org/10.2166/WCC.2024.699.

Lawrimore, J.H., Menne, M.J., Gleason, B.E., Williams, C.N., Wuertz, D.B., Vose, R.S., Rennie, J., 2011. An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res. Atmos. https://doi.org/10.1029/2011JD016187.

Lee, S., Moriasi, D.N., Danandeh Mehr, A., Mirchi, A., 2024. Sensitivity of Standardized Precipitation and Evapotranspiration Index (SPEI) to the choice of SPEI probability distribution and evapotranspiration method. J. Hydrol. Reg. Stud. 53, 101761. https://doi.org/10.1016/j.EJHR.2024.101761.

Li, X., Sha, J., Wang, Z.L., 2019. Comparison of drought indices in the analysis of spatial and temporal changes of climatic drought events in a basin. Environ. Sci. Pollut. Res. 26. https://doi.org/10.1007/s11356-019-04529-z.

Li, L., She, D., Zheng, H., Lin, P., Yang, Z.L., 2020. elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China. J. Hydrometeorol. 21. https://doi.org/10.1175/JHM-D-19-0290.s1.
Likoya, E., Birch, C.E., Chapman, S., Dougill, A.J., 2023. Austral summer droughts and their driving mechanisms in observations and present-day climate simulations over Malawi. Int. J. Clim. 43. https://doi.org/10.1002/joc.8137.

Lim Kam Sian, K.T.C., Zhi, X., Ayugi, B.O., Onyutha, C., Shilenje, Z.W., Ongoma, V., 2023. Meteorological drought variability over Africa from multisource datasets. Atmosphere 14. https://doi.org/10.3390/atmos14061052.

Liu, X., Zhu, X., Zhang, Q., Yang, T., Pan, Y., Sun, P., 2020. A remote sensing and artificial neural network-based integrated agricultural drought index: Index development and applications. Catena 186. https://doi.org/10.1016/j.catena.2019.104394.

Lombe, P., Carvalho, E., Sustainability, P.R.-S., 2024. undefined, 2024. Drought dynamics in sub-Saharan Africa: impacts and adaptation strategies. mdpi. ComP. Lombe E Carvalho P RosaSt. 2024•mdpi.com.

Maidment, R.I., Allan, R.P., Black, E., 2015. Recent observed and simulated changes in precipitation over Africa. Geophys. Res. Lett. 42 (19), 8155–8164.

Maidment, R.I., Grimes, D., Allan, R.P., Tarnavsky, E., Marcstringer, M., Hewison, T., Roebeling, R., Black, E., 2014. The 30 year TAMSAT african rainfall climatology and time series (TARCAT) data set. J. Geophys. Res. 119. https://doi.org/10.1002/2014JD021927.

Maidment, R.I., Grimes, D., Black, E., Tarnavsky, E., Young, M., Greatrex, H., Alcántara, E.M.U., 2017. A new, long-term daily satellite-based rainfall dataset for operational monitoring in Africa. Sci. data 4 (1), 1–19.

Makokha, J., Masayi, N., Barasa, P., ... P.I., 2024. undefined, 2024. Assessing the Long-Term Changes in Selected Meteorological Parameters over the North-Rift, Kenya: A Regional Climatology Perspective. Res. Makokha NN Masayi P Barasa PA Ikoha MM Konje J. Mutonyi VS Okello WechuliHydrology, 2024•researchgate.net.

Mankin, K.R., Mehan, S., Green, T.R., Barnard, D.M., 2025. Review of gridded climate products and their use in hydrological analyses reveals overlaps, gaps, and the need for a more objective approach to selecting model forcing datasets. Hydrol. Earth Syst. Sci. 29 (1), 85–108.

Mann, H.B., 1945. Nonparametric Tests Against Trend. Econometrica 13. https://doi.org/10.2307/1907187.

Maranz, S., 2009. Tree mortality in the African Sahel indicates an anthropogenic ecosystem displaced by climate change. J. Biogeogr. 36. https://doi.org/10.1111/j.1365-2699.2008.02081.x.

Masih, I., Maskey, S., Mussá, F.E.F., Trambauer, P., 2014. A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol. Earth Syst. Sci. 18 (9), 3635–3649.

Mathbout, S., Lopez-Bustins, J.A., Royé, D., Martin-Vide, J., 2021. Mediterranean-scale drought: Regional datasets for exceptional meteorological drought events during 1975-2019. Atmosphere (Basel) 12. https://doi.org/10.3390/atmos12080941.

Matimolane, S., Strydom, S., Mathivha, F.I., Chikoore, H., 2024. Evaluating the spatiotemporal patterns of drought characteristics in a semi-arid region of Limpopo Province, South Africa. 2024•Springer Springer Matimolane S Strydom FI Mathivha H. ChikooreEnvironmental Monit. Assess. 196, 1062. https://doi.org/10.1007/S10661-024-13217-6.

Maure, G., Pinto, I., Ndebele-Murisa, M., Muthige, M., Lennard, C., Nikulin, G., Dosio, A., Meque, A., 2018. The southern African climate under 1.5 °C and 2 °C of global warming as simulated by CORDEX regional climate models. Environ. Res. Lett. 13. https://doi.org/10.1088/1748-9326/aab190.

McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales, in: Proc. 8th Conf. on Applied Climatology, Anaheim, California, 179–184. Eighth Conf. Appl. Climatol.

McVicar, T.R., Roderick, M.L., Donohue, R.J., Li, L.T., Van Niel, T.G., Thomas, A., Grieser, J., Jhajharia, D., Himri, Y., Mahowald, N.M., Mescherskaya, A.V., Kruger, A.C., Rehman, S., Dinpashok, Y., 2012. Global review and synthesis of trends in observed terrestrial near-surface wind speeds: Implications for evaporation. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2011.10.024.

Mengistu, A.G., Tesfuhuney, W.A., Woyessa, Y.E., van Rensburg, L.D., 2020. Analysis of the spatio-temporal variability of precipitation and drought intensity in an arid catchment in South Africa. Climate 8. https://doi.org/10.3390/CLI8060070.

Meyer, J.J.M., Potgieter, M.M., Meyer, N.L., Meyer, A.C., 2025. Climate Change-Induced Decline in Succulent Euphorbia in Namibia's Arid Regions, 190 14 Plants 2025 14, 190. https://doi.org/10.3390/PLANTS14020190.

Milly, P.C.D., Dunne, K.A., 2016. Potential evapotranspiration and continental drying. Nat. Clim. Chang. 6. https://doi.org/10.1038/nclimature3046.

Misnawati, Boer, R., Ramdhani, F., 2021. Spatiotemporal Variation of Drought Characteristics Based on Standardized Precipitation Index in Central Java over 1990-2010. IOP Conference Series Earth Environmental Science. https://doi.org/10.1088/1755-1315/893/1/012022.

Monerie, P.A., Biasutti, M., Mignot, J., Mohino, E., Pohl, B., Zappa, G., 2023. Storylines of Sahel Precipitation Change: Roles of the North Atlantic and Euro-Mediterranean Temperature. J. Geophys. Res. Atmos. 128. https://doi.org/10.1029/2023JD038712.

Monerie, P.A., Sanchez-Gomez, E., Gaetani, M., Mohino, E., Dong, B., 2020a. Future evolution of the Sahel precipitation zonal contrast in CESM1. Clim. Dyn. 55. https://doi.org/10.1007/s00382-020-05417-w.

Mukherjee, S., Mishra, A., Trenberth, K.E., 2018. Climate Change and Drought: a Perspective on Drought Indices. Curr. Clim. Chang. Rep. https://doi.org/10.1007/s04641-018-0098-x.

Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R.A., Carrao, H., Spinoni, J., Vogt, J., Feyen, L., 2018. Global Changes in Drought Conditions Under Different Levels of Warming. Geophys. Res. Lett. 45. https://doi.org/10.1002/2017GL076521.

Naumann, G., Dutra, E., Barbosa, P., Pappenberger, F., Wetterbergh, F., Vogt, J.V., 2014. Comparison of drought indicators derived from multiple data sets over Africa. Hydrol. Earth Syst. Sci. 18. https://doi.org/10.5194/hess-18-1625-2014.

Ndayiragije, J.M., Li, F., 2022. Effectiveness of Drought Indices in the Assessment of Different Types of Droughts, Managing and Mitigating Their Effects. Climate. https://doi.org/10.3390/cli10090125.

Negrón Juárez, R.I., Li, W., Fernandes, K., de Oliveira Cardoso, A., 2009. Comparison of precipitation data sets over the tropical South American and African continents. J. Hydrometeorol. 10. https://doi.org/10.1175/2008JHM1023.1.

Nicholson, S.E., 2013. The West African Sahel: A Review of Recent Studies on the Rainfall Regime and Its Interannual Variability. ISRN Meteor. 2013. https://doi.org/10.1155/2013/453521.

Nicholson, S.E., Funk, C., Fink, A.H., 2018. Rainfall over the African continent from the 19th through the 21st century. Glob. Planet. Change 165. https://doi.org/10.1016/j.gloplacha.2017.12.014.

Nkemelang, T., New, M., Research, M.Z.-E., 2018. undefined, 2018. Temperature and precipitation extremes under current, 1.5C and 2.0C global warming above pre-industrial levels over Botswana, and implications for climate change. iopscience.iop.org T Nkemelang 2018•iopscience.iop.org M N M ZarougEnvironmental Res. Lett. https://doi.org/10.1088/1748-9326/AAC2F8/META.

Ogunrinde, A.T., Adigun, P., Xue, X., Koji, D., Jing, Q., 2025. Spatiotemporal analysis of drought patterns and trends across Africa: a multi-scale SPEI approach (1960–2018), 2025•Taylor Fr Taylor Fr. Ogunrinde P Adigun X Xue D. Koji Q JingInternational J. Digit. Earth 18, 2447342. https://doi.org/10.1080/17538947.2024.2447342.

Overpeck, J.T., 2013. The challenge of hot drought. Nature 503 (7476), 350–351.

Palmer, P.I., Wainwright, C.M., Dong, B., Maidment, R.I., Wheeler, K.G., Gedney, N., Hickman, J.E., Madani, N., Folwell, S.S., Abdo, G., Allan, R.P., Black, E.C.L., Feng, L., Gudoshava, M., Haines, K., Huntingford, C., Kilavi, M., Lunt, M.F., Shaaban, A., Turner, A.G., 2023. Drivers and impacts of Eastern African rainfall variability. Nat. Rev. Earth Environ. 4 (4), 254–270.

Panthou, G., Lebel, T., Vischel, T., Quantin, G., Sane, Y., Ba, A., Ndiaye, O., Diongue-Niang, A., Diopkane, M., 2018. Rainfall intensification in tropical semi-arid regions: The Sahelian case. Environ. Res. Lett. 13. https://doi.org/10.1088/1748-9326/aac334.

Paxian, A., Sein, D., Panitz, H.J., Warscher, M., Breil, M., Engel, T., Tödter, J., Krause, A., Cabos Narvaez, W.D., Fink, A.H., Ahrens, B., Kunstmann, H., Jacob, D., Paeth, H., 2016. Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models. J. Geophys. Res. 121. https://doi.org/10.1002/2015JD024143.

Pei, W., Fu, Q., Liu, D., Li, T., 2019. A drought index for Rainfed agriculture: The Standardized Precipitation Crop Evapotranspiration Index (SPCEI). Hydrol. Process 33. https://doi.org/10.1002/hyp.13365.

Pereira, C.A., Martins, J.P., Fink, A.H., Pinto, J.G., Ramos, A.M., 2024. Drivers of seasonal rainfall variability over the Angolan and Namibian plateaus, 2024•Wiley Online Libr. Wiley Online Libr. Pereira JP Martins AH Fink JG Pinto AM RamosInternational J. Clim. https://doi.org/10.1002/JOC.8545.
Perez, M., Lombardi, D., Bardino, G., Indicators, M.V.-E., 2024, undefined, 2024. Drought assessment through actual evapotranspiration in Mediterranean vegetation dynamics. ElsevierM Perez, D. Lomb, G Bardino, M VitaleEcological Indic. 2024•Elsevier.

Preethi, B., Revadekar, J.V., Munot, A.A., 2011. Extremes in summer monsoon precipitation over India during 2001-2009 using CPC high-resolution data. Int. J. Remote Sens 32. https://doi.org/10.1080/01431161.2010.517795.

Randriamarolaza, L.Y.A., Aguilar, E., Skrynyk, O., Vicente-Serrano, S.M., Domínguez-Castro, F., 2022. Indices for daily temperature and precipitation in Madagascar, based on quality-controlled and homogenized data, 1950–2018. Int. J. Clim. 42. https://doi.org/10.1002/joc.7243.

Randriatsara, H.H.-R.H., Holtanova, E., Rizwan, K., Babaousmail, H., Rabezanahary, M.F.T., Posset, K.R., Alupot, D., Ayugi, B.O., 2025. Historical changes in drought characteristics and their impact on vegetation cover over Madagascar. Nat. Hazards Earth Syst. Sci. 25, 2939–2961. https://doi.org/10.5194/NHESS-25-2939-2025.

Reason, C.J.C., Jagadheesha, D., 2005. A model investigation of recent ENSO impacts over southern Africa. Meteorol. Atmos. Phys. 89. https://doi.org/10.1007/s00703-005-0128-9.

Rigden, A., Golden, C., Chan, D., Huybers, P., 2024. Climate change linked to drought in Southern Madagascar. npj Clim. Atmos. Sci. 7. https://doi.org/10.1038/s41612-024-00583-8.

Rusca, M., Savelli, E., Di Baldassarre, G., Biza, A., Messori, G., 2023. Unprecedented droughts are expected to exacerbate urban inequalities in Southern Africa. Nat. Clim. Chang 13. https://doi.org/10.1038/s41558-022-01546-8.

Salvador, C., Nieto, R., Vicente-Serrano, S.M., García-Herrera, R., Gimeno, L., Vicedo-Cabrera, A.M., 2023. Public Health Implications of Drought in a Climate Change Context: A Critical Review. Annu. Rev. Public Health. https://doi.org/10.1146/annurev-publhealth-071421-051636.

Sawunyama, T., Hughes, D.A., 2008. Application of satellite-derived rainfall estimates to extend water resource simulation modelling in South Africa. Water SA 34. https://doi.org/10.4314/wsa.v3a41i180739.

Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., 2018. GPCC Full Data Monthly Product Version 2018 at 0.5e: Monthly land-surface precipitation from rain-gauges built on GTS-based and historical data. Glob. Precip. Climatol. Cent.

Schwabe, K., Albiac, J., Connor, J.D., Rashid, Hassan, M., Meza González Editors, L., 2015. Drought in arid and semi-arid regions. https://doi.org/10.1007/978-94-007-6636-5.PDF.

Sen, P.K., 1968. Estimates of the Regression Coefficient Based on Kendall's Tau. J. Am. Stat. Assoc. 63. https://doi.org/10.1080/01621459.1968.10480934.

Sheffield, J., Wood, E.F., Roderick, M.L., 2012. Little change in global drought over the past 60 years. Nature 491. https://doi.org/10.1038/nature11575.

Sheffield, J., Wood, E.F., Chaney, N., Guan, K., Sadri, S., Yuan, X., Olang, L., Amani, A., Ali, A., Demuth, S., Ogallo, L., 2014. A drought monitoring and forecasting system for sub-Saharan African water resources and food security. Bull. Am. Meteorol. Soc. 95 (6), 861–882.

Spinage, C.A., 2012. The Changing Climate of Africa Part III: Southern Africa. https://doi.org/10.1007/978-3-642-22872-8-4.

Spinoni, J., Barbosa, P., Bucchignani, E., Cassano, J., Cavazos, T., Christensen, J.H., Christensen, O.B., Coppola, E., Evans, J., Geyer, B., Giorgi, F., Hadjinicolauo, P., Jacob, D., Katzfey, J., Koenig, T., Laprise, R., Lennard, C.J., Kurnaz, M.L., Delei, L.I., Llopart, M., McCormick, N., Naumann, G., Nikulin, G., Ozturk, T., Panitz, H. J., da Rocha, R.P., Rockel, B., Solman, S.A., Syktus, J., Tangard, F., Teichmann, C., Vautard, R., Vogt, J.V., Winger, K., Zittis, G., Dosio, A., 2020. Future global meteorological drought hot spots: A study based on CORDEX data. J. Clim. 33. https://doi.org/10.1175/JCLI-D-19-0084.1.

Spinoni, J., Naumann, G., Carrao, H., Barbosa, P., Vogt, J., 2014. World drought frequency, duration, and severity for 1951-2010. Int. J. Clim. 34. https://doi.org/10.1002/joc.3875.

Stefanidis, S., Rossiou, D., Proutsos, N., 2023. Drought Severity and Trends in a Mediterranean Oak Forest. Hydrology 10. https://doi.org/10.3390/hydrology10080167.

Stisen, S., Sandholt, I., 2010. Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modelling. Hydrol. Process.: Int. J. 24 (7), 879–891.

Tall, M., Sylla, M.B., Dajuma, A., Almazroui, M., Houteta, D.K., Klutse, N.A.B., Dosio, A., Lennard, C., Driouech, F., Diedhiou, A., Giorgi, F., 2023. Drought variability, changes and hot spots across the African continent during the historical period (1928–2017). Int. J. Clim. 43. https://doi.org/10.1002/joc.8293.

Tanarhte, M., De Vries, A.J., Zittis, G., Chfadi, T., 2024. Severe droughts in North Africa: A review of drivers, impacts and management. EarthSci. Rev. https://doi.org/10.1016/j.earscirev.2024.104701.

Tefera, A.S., Ayoade, J.O., Bello, N.J., 2019. Comparative analyses of SPI and SPEI as drought assessment tools in Tigray Region, Northern Ethiopia. SN Appl. Sci. 1. https://doi.org/10.1007/s42452-019-1326-2.

Tegegne, G., Alemayehu, S., Dejene, S., Climate, L.G., 2025. undefined, 2025. Next-generation drought intensity-duration-frequency curves for early warning systems in Ethiopia's pastoral region. mdpi. com Tegegne S Alemayehu SW Dejene L Gebre TT Zeleke L Tesfaye N. AbdulhamidClimate 2025•mdpi. Com.

Thornthwaite, C.W., 1948. An Approach toward a Rational Classification of Climate. Geogr. Rev. 38. https://doi.org/10.2307/210739.

Tomas-Burguera, M., Vicente-Serrano, S.M., Peña-Angulo, D., Domínguez-Castro, F., Noguera, I., El Kenawy, A., 2020. Global Characterization of the Varying Responses of the Standardized Precipitation Evapotranspiration Index to Atmospheric Evaporative Demand. J. Geophys. Res. Atmos. 125. https://doi.org/10.1029/2020JD033011.

Törnros, T., Menzel, L., 2014. Addressing drought conditions under current and future climates in the Jordan River region. Hydrol. Earth Syst. Sci. 18. https://doi.org/10.5194/hess-18-305-2014.

Tramblay, Y., Rueland, D., Hanich, L., Dakhlaoui, H., 2018. Sub-chapter 2.3.1. Hydrological impacts of climate change in North African countries. Mediterr. Reg. Clim. Change. https://doi.org/10.4000/books.irditions.23496.

Uhe, P., Philip, S., Kew, S., Shah, K., Kimutai, J., Mwangi, E., van Oldenborgh, G.J., Singh, R., Arrighi, J., Jjemba, E., Cullen, H., Otto, F., 2018. Attributing drivers of the 2016 Kenyan drought. Int. J. Clim. 38. https://doi.org/10.1002/joc.5389.

Vicente-Serrano, S.M., Beguería, S., López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Clim. 23. https://doi.org/10.1175/2009JCLI2909.1.

Vicente-Serrano, S.M., McVicar, T.R., Miralles, D.G., Yang, Y., Tomas-Burguera, M., 2020. Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change. Wiley Interdiscip. Rev. Clim. Chang. https://doi.org/10.1002/wcc.632.

Vicente-Serrano, S.M., Tramblay, Y., Reig, F., González-Hidalgo, J.C., Beguería, S., Brunetti, M., Kalin, K.C., Patalen, L., Kržić, A., Lionello, P., Lima, M.M., Trigo, R.M., El-Kenawy, A.M., Eddenjal, A., Türkes, M., Koutroulis, A., Manara, V., Maugeri, M., Badi, W., Mathbout, S., Bertalanic, R., Bocheva, L., Dabanli, I., Dumitrescu, A., Duboisson, B., Sahabi-Abed, S., Abdulla, F., Fayad, A., Hodzic, S., Ivanov, M., Radevski, I., Peña-Angulo, D., Lorenzo-Lacruz, J., Domínguez-Castro, F., Gimeno-Sotelo, L., García-Herrera, R., Franquesa, M., Halifa-Marín, A., Adell-Michavila, M., Noguera, I., Barriopedro, D., Garrido-Perez, J.M., Azorin-Molina, C., Andres-Martin, M., Llasat, M.C., Markonis, Y., Selmi, R., Rached, S., Ben, Radovanović, S., Soubeyroux, J.-M., Ribes, A., Saidi, M.E., Bataineh, S., Mahdi, E., Khalki, E., Robaa, S., Boucetta, A., Alsfadi, K., Mamassis, N., Mohammed, S., Fernández-Duque, B., Cheval, S., Moutia, S., Stevkov, A., Stevkova, S., Luna, M.Y., Potopová, V., 2025. High temporal variability not trend dominates Mediterranean precipitation. Nat. comSM VicenteSerrano Y Tramblay F. Reig JC GonzáLez. Hidalgo S Beguería M BrunettiNature 2025•Nat. Com. 639. https://doi.org/10.1038/s41586-024-08576-6.

Vose, R.S., Arndt, D., Banzon, V.F., Easterling, D.R., Gleason, B., Huang, B., Kearns, E., Lawrimore, J.H., Menne, M.J., Peterson, T.C., Reynolds, R.W., Smith, T.M., Williams, C.N., Wuertz, D.B., 2012. NOAA's Merged Land-Ocean Surface Temperature Analysis. Bull. Am. Meteorol. Soc. 93, 1677–1685. https://doi.org/10.1175/BAMS-D-11-00241.1.

Walker, D.W., Van Loon, A.F., 2023. Droughts are coming on faster. Science 307. https://doi.org/10.1126/science.adh3097.

Wan, L., Bento, V.A., Qu, Y., Qiu, J., Song, H., Zhang, R.R., Wu, X., Xu, F., Lu, J., Wang, Q., 2023. Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018. Sci. Total Environ. 901. https://doi.org/10.1016/j.scitotenv.2023.166362.

Wang, K.C., Dickinson, R.E., Wild, M., Liang, S., 2012. Atmospheric impacts on climatic variability of surface incident solar radiation. Atmos. Chem. Phys. 12. https://doi.org/10.5194/acp-12-9581-2012.

Wang, B., Ding, Q., Fu, X., Kang, I.S., Jin, K., Shukla, J., Doblas-Reyes, F., 2005. Fundamental challenge in simulation and prediction of summer monsoon rainfall. Geophys. Res. Lett. 32. https://doi.org/10.1029/2005GL022734.
Wang, T., She, D., Bao, Z., Zhang, Q., Wang, L., Wei, Y., Niu, Q., 2025. Elucidating diverse population exposure to compound drought and heatwave events from two meteorological drought indices (SPI and SPEI). Environ. Res. Lett. 20, 034027. https://doi.org/10.1088/1748-9326/ADAD01.

Wei, W., Wang, J., Wang, X., Yan, P., Xie, B., Zhou, J., Liu, T., Lu, D., 2024. The response of global terrestrial water storage to drought based on multiple climate scenarios. Atmos. Res. 303. https://doi.org/10.1016/j.atmosres.2024.107331.

Worku, M.A., 2024. Spatiotemporal analysis of drought severity using SPI and SPEI: case study of semi-arid Borana area, southern Ethiopia. Front. Environ. Sci. 12. https://doi.org/10.3389/fenvs.2024.1337190.

Wubaye, G.B., Gashaw, T., Worqlul, A.W., Dile, Y.T., Taye, M.T., Hailelassie, A., Zaitchik, B., Birhan, D.A., Adgo, E., Mohammed, J.A., Lebeza, T.M., Bantider, A., Seid, A., Srinivasan, R., 2023. Trends in Rainfall and Temperature Extremes in Ethiopia: Station and Agro-Ecological Zone Levels of Analysis. Atmosphere (Basel) 14. https://doi.org/10.3390/atmos14030483.

Xie, P., Arkin, P.A., 1997. Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs. Bull. Am. Meteorol. Soc. 78. https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

Xie, P., Arkin, P.A., Janowiak, J.E., 2007. CMAP: The CPC Merged Analysis of Precipitation. In: Levizzani, V., Bauer, P., Turk, F.J. (Eds.), Measuring Precipitation From Space. Advances In Global Change Research, 28. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5835-6_25.

Xie, P., Joyce, R., Wu, S., Yoo, S.H., Yarosh, Y., Sun, F., Lin, R., 2017. Reprocessed, bias-corrected CMORPH global high-resolution precipitation estimates from 1998. J. Hydrometeorol. 18. https://doi.org/10.1175/JHM-D-16-0168.1.

Yahaya, I., Zhou, J., Jiang, S., Xu, R.H., Huang, J., Su, B., Jing, C., Qiu, F., Jiang, T., 2024. Depiction of Drought Over Africa in the Light of Changing Climate from CMIP6 Models, 2024•Springer Springe Yahaya J. Zhou S Jiang RH Xu J. Huang B Su C. Jing F. Qiu T JiangEarth Syst. Environ. 8, 1011–1035. https://doi.org/10.1007/S41748-024-00502-0.

Yang, Z., Zhang, Q., Hao, X., Yue, P., 2019. Changes in Evapotranspiration Over Global Semiarid Regions 1984–2013. J. Geophys. Res. Atmos. 124. https://doi.org/10.1029/2018JD029533.

Yevjevich, V., 1967. An objective approach to definitions and investigations of continental hydrologic droughts. Colorado State University.

Yin, X., Gruber, A., Arkin, P., 2004. Comparison of the GPCP and CMAP merged gauge-satellite monthly precipitation products for the period 1979-2001. J. Hydrometeorol. 5. https://doi.org/10.1175/JHM-392.1.

Zeng, N., 2003. Drought in the Sahel. Science 302 (5647), 999–1000.

Zhou, Z., Wang, P., Li, L., Fu, Q., Ding, Y., Chen, P., 2024. undefined, 2024. Recent development on drought propagation: A comprehensive review (P.X.-J. of). ElsevierZ Zhou P Wang L. Li Q Fu Y Ding P Chen P Xue T Wang H. ShiJournal Hydrol. 2024•Elsevier.

Zhou, J., Wang, Y., Su, B., Wang, A., Tao, H., Zhai, J., Kundzewicz, Z.W., Jiang, T., 2020. Choice of potential evapotranspiration formulas influences drought assessment: A case study in China. Atmos. Res. 242. https://doi.org/10.1016/j.atmosres.2020.104979.

Ziese, M., Schneider, U., Meyer-Christoffer, A., Schamm, K., Vido, J., Finger, P., Bissolli, P., Pietzsch, S., Becker, A., 2014. The GPCC Drought Index - A new, combined and gridded global drought index. Earth Syst. Sci. Data 6. https://doi.org/10.5194/essd-6-285-2014.

Zomer, R.J., Xu, J., Trabucco, A., 2022. Version 3 of the global aridity index and potential evapotranspiration database. Sci. Data 9. https://doi.org/10.1038/s41597-022-01493-1.