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Article

Extreme Weather Patterns in Ethiopia: Analyzing Extreme Temperature and Precipitation Variability

by
Endris Ali Mohammed
1,2,
Xiefei Zhi
1,* and
Kemal Adem Abdela
3
1
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, School of Atmospheric Sciences, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
2
Ethiopian Meteorological Institute, Addis Ababa P.O. Box 1090, Ethiopia
3
School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(2), 133; https://doi.org/10.3390/atmos16020133
Submission received: 19 December 2024 / Revised: 17 January 2025 / Accepted: 24 January 2025 / Published: 27 January 2025
(This article belongs to the Special Issue Understanding and Forecasting Seasonal Weather and Climate Extreme Events)
Browse Figures
Figure 1
<p>Map of Ethiopia Highlighting 103 Meteorological Stations Elevation (m).</p> "> Figure 2
<p>Shows scatter plots for data validation between observed values (ground measurements) and satellite datasets for (<b>a</b>) rainfall, (<b>b</b>) maximum temperature and (<b>c</b>) minimum temperature on a monthly time scale in Ethiopia from 1998 to 2020.</p> "> Figure 3
<p>Spatial patterns of severe temperature indices in Ethiopia from 1994 to 2023 include (<b>a</b>) TXx, (<b>b</b>) TXn, (<b>c</b>) TNx, (<b>d</b>) TNn, (<b>e</b>) TX10p, (<b>f</b>) TN10p, (<b>g</b>) TN90p, and (<b>h</b>) DTR. Upward green and black triangles indicate significant and non-significant increasing trends, respectively, while downward blue and red triangles represent significant and non-significant decreasing trends. Yellow circles indicate no trend. ‘Sig’ and ‘No Sig’ denote significant and non-significant trends.</p> "> Figure 4
<p>This time series displays the annual average trends of severe temperature indicators in Ethiopia, including (<b>a</b>) TXx, (<b>b</b>) TXn, (<b>c</b>) TNx, (<b>d</b>) TNn, (<b>e</b>) TX10p, (<b>f</b>) TN10p, (<b>g</b>) TN90p, and (<b>h</b>) DTR over time.</p> "> Figure 5
<p>Spatial patterns of severe temperature indices in Ethiopia from 1994 to 2023, including (<b>a</b>) CDD, (<b>b</b>) CWD, (<b>c</b>) PRCPTOT, (<b>d</b>) RX1day, (<b>e</b>) RX5day, (<b>f</b>) R10mm, (<b>g</b>) R95p and (<b>h</b>) R99p. Upward green and black triangles indicate significant and non-significant increasing trends, respectively, while downward blue and red triangles represent significant and non-significant decreasing trends. Yellow circles indicate no trend. ‘Sig’ and ‘No Sig’ denote significant and non-significant trends.</p> "> Figure 6
<p>This time series displays the annual average trends of severe precipitation indicators in Ethiopia, including (<b>a</b>) CDD, (<b>b</b>) CWD, (<b>c</b>) PRCPTOT, (<b>d</b>) RX1day, (<b>e</b>) RX5day, (<b>f</b>) R10mm, (<b>g</b>) R95p and (<b>h</b>) R99p over time.</p> "> Figure 7
<p>This displays the three key modes of empirical orthogonal functions (EOFs) and principal component analyses (PCAs) for seasonal precipitation during the JJAS (<b>a</b>–<b>f</b>) periods. (The red color represents positive anomalies, while blue indicates negative anomalies).</p> "> Figure 8
<p>This displays the three key modes of empirical orthogonal functions (EOFs) and principal component analyses (PCAs) for seasonal precipitation during the FMAM (<b>a</b>–<b>f</b>) periods. (The red color represents positive anomalies, while blue indicates negative anomalies).</p> ">
Versions Notes

Abstract

:
Climate change is significantly altering Ethiopia’s weather patterns, causing substantial shifts in temperature and precipitation extremes. This study examines historical trends and changes in extreme rainfall and temperature, as well as seasonal rainfall variability across Ethiopia. In this study, we employed the Mann–Kendall test, Sen’s slope estimator, and empirical orthogonal function (EOF), with data from 103 stations (1994–2023). The findings provide insights into 16 climate extremes of temperature and precipitation by utilizing the climpact2.GUI tool in R software (v1.2). The study found statistical increases were observed in 59.22% of the annual maximum value of daily maximum temperature (TXx) and 77.67% of the annual maximum value of daily minimum temperature (TNx). Conversely, decreasing trends were found in 51.46% of the annual maximum daily maximum temperature (TXn) and 85.44% of the diurnal temperature range (DTR). The results of extreme precipitation found that 72.82% of yearly total precipitation (PRCPTOT), 73.79% of consecutive wet days (CWD), and 54.37% of the number of heavy precipitation days (R10mm) showed increasing trends. In contrast, at most selected stations, 61.17% of consecutive dry days (CDD), 55.34% of maximum 1-day precipitation (RX1day), 56.31% of maximum 5-day precipitation (RX5day), 66.02% of precipitation from very wet days (R95p), and 52.43% of precipitation from extremely wet days (R99p) were decreasing. The results of seasonal precipitation variability during Ethiopia’s JJAS (Kiremt) season found that the first three EOF modes accounted for 59.78% of the variability. Notably, EOF1, which accounted for 35.84% of this variability, showed declining rainfall patterns, particularly in northwestern and central-western Ethiopia. The findings of this study will help policymakers and stakeholders understand these changes and take necessary action, as well as build effective adaptation and mitigation measures in the face of climate change impacts.

1. Introduction

Climate change is often regarded as the most critical environmental concern of the twenty-first century, a reality that is likely to persist [1]. It has significantly increased the frequency and severity of heavy rainfall events, leading to heightened risks of floods and exacerbating drought conditions globally [2,3]. This alteration in precipitation patterns is profound, resulting in more extreme weather phenomena. A notable instance is the 2022 drought in the Po Valley, which has been classified as the worst in two centuries [4]. The increased frequency and severity of heavy rainfall events leads to flooding and droughts, endangering ecosystems and biodiversity. These severe weather events damage natural environments, causing significant losses in plant and animal populations [5]. Furthermore, the combination of increasing precipitation, floods, and soil erosion causes significant harm to human infrastructure and the surrounding ecosystems [6]. The warmer atmosphere holds more moisture, resulting in more extreme weather events [7].
Extreme precipitation and temperature are key indicators of climate change [8]. As global mean temperatures significantly increase up to 5 °C above pre-industrial levels, the associated impacts and risks escalate dramatically [9]. This warming trend correlates with a notable global increase in rainfall, with trends showing a 1–2% rise per decade between 1951 and 2010 [10,11]. Such rising temperatures and changing precipitation patterns pose significant challenges to water resources, public health, and socio-economic stability [12]. Since the 1950s, extreme rainfall events have surged due to human-induced climate change, resulting in more pluvial floods. Concurrently, droughts have intensified, especially in the Horn of Africa, critically undermining food security and affecting millions of people [13,14]. Climate-related events account for over 70% of global disasters, with extreme rainfall events like droughts and floods being a major factor [15,16]. Over the past five decades, these disasters have become regular, causing daily deaths of at least 115 people and $202 million in economic losses [17].
Extreme temperatures have a significant health impact, contribute to wildfires, and reduce agricultural production [18,19,20]. These extreme climatic changes have a particularly substantial influence on agriculture, since rain fed systems account for approximately 80% of farmed area. These systems are vital, accounting for around 60% of world food production [21]. In Ethiopia, climatic hazards caused by rainfall variability cause recurring droughts and floods, severely impacting agricultural output and compromising food security [22]. These extremes disrupt agricultural productivity and threaten food security, highlighting the multifaceted impacts of climate extremes [23]. Droughts have become more frequent and extreme, particularly in desert regions [24]. Annually, almost three million Ethiopians are food insecure due to severe drought conditions, while a million people are impacted by floods [25]. For instance, in 2020 this affected 1.1 million people and displaced 342,000 [26]. This vulnerability to climate change is exacerbated by high poverty levels, rain fed agriculture, and limited adaptive capacity to climate change [27]. Additionally, rapid increases in population, vector-borne illnesses, and environmental deterioration all exacerbate these issues [28]. Several factors contribute to the occurrence of climate extremes in Ethiopia, including atmospheric circulation, topography, and human activity [29]. Ethiopia has seen an increase in the number of days with precipitation, but fewer continuous rainy days [30]. Meanwhile, the frequency of cold nights, cold days, and frost days has decreased across the country, while warm evenings, warm days, and summer days have increased [31]. As a result, the examination of changes in extreme climate in terms of precipitation and temperature is especially important for Ethiopia, since agriculture and food security are sensitive sectors due to the variability, severity, and frequency of extreme climate [32]. The lack of long-term daily climatic data presents significant challenges for assessing climate change implications in Ethiopia [33]. This shortage makes it difficult to grasp the regional and temporal variation in temperature and precipitation extremes. Extreme weather and climate events continue to be difficult to study, particularly in Ethiopia, due to a lack of or limited access to observed station data at the daily level. Overcoming these data constraints is critical for correctly estimating the consequences of climate change and establishing effective adaptation solutions.
This study examines historical trends in annual extreme rainfall and temperature indices, as well as seasonal rainfall variability across Ethiopia. This finding will provide vital insights that can directly inform adaptation and mitigation strategies within Ethiopian communities. Specifically, the research will enhance Early Warning Systems to improve community preparedness for extreme weather, enable Informed Water Resource Management to optimize water use during droughts, support Agricultural Adaptation Strategies to ensure food security, contribute to building community resilience against climate impacts, and promote socioeconomic improvement through effective adaptation planning.

2. Study Area, Data, Quality Control and Methods

2.1. Study Area

Ethiopia is located in the Horn of Africa, between 3° N and 15° N, and 33° E to 48° E. It has borders with Eritrea to the north, Djibouti to the east, Sudan to the west, Kenya to the south, and Somalia to the south and east (Figure 1). It has a diversified terrain that covers 1.14 million square kilometers. Ethiopia has three rainy seasons: Bega, Belg, and Kiremt. The Kiremt season, which runs from June to September, is critical to agriculture, providing 50–80% of the country’s yearly rainfall. The Belg season, which runs from February to May, is uncertain yet vital for soil preparation. Bega occurs between October and December with drier and colder weather. The Inter-Tropical Convergence Zone (ITCZ) movement influences Ethiopian seasonal rainfall [34]. The precipitation distribution is variable, with the southeastern and northeastern lowlands receiving less than 300 mm of rain per year, while the southwestern highlands receive approximately 2000 mm. The average annual temperature varies from below 15 °C in the highlands to over 25 °C in the lowlands.

2.2. Data

Due to the unavailability of daily recorded minimum and maximum temperatures from the 103 stations in the research area, meteorological data were obtained from the National Aeronautics and Space Administration (NASA). It has long funded satellite systems that provide critical data for climate study via its Earth Science program. This program presents long-term averaged climate variables and surface solar energy fluxes in a time series style, with mean daily values. Satellite- and model-based technologies have shown to be trustworthy sources of accurate solar and meteorological data in locations where surface observations are restricted or nonexistent. Previous studies have confirmed this climatic data, giving it legitimacy for research purposes [35]. It is accessible at (https://power.larc.nasa.gov/data-access-viewer/, accessed on 10 September 2024) (Table S1). In addition, daily precipitation data from the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data) dataset for the period 1994 to 2023 were extracted using the Climate Data Tool (CDT) across 103 meteorological stations for this study. CHIRPS combines climatology, high-resolution (0.05° × 0.05°) satellite imagery, and in situ station data to create a comprehensive gridded rainfall time series. This information is essential for trend analysis and seasonal drought monitoring, as it provides valuable insights into precipitation patterns over time [36]. The link to access this data is (https://data.chc.ucsb.edu/products/CHIRPS-2.0, accessed on 15 October 2024).

2.3. Data Quality Control

To ensure the reliability of trend evaluations, data quality control is required before computing extreme indices. Any misleading outliers may distort the patterns. In this study, we used the R-based statistical software RClimDex v1.0 to check data quality [37]. RClimDex provides a simple R interface for computing the 27 major indices of severe climate specified by ETCCDI. This program detects missing data, duplicate dates, and out-of-range values (based on a preset threshold), corrects errors, and handles outliers in daily temperature (Tmax > Tmin) and precipitation records. RClimDex’s major purpose is to monitor and identify climate change using these computations. An outlier is defined as the mean plus or minus n times the daily standard deviation (mean – n × std, mean + n × std). The user flags values that fall outside of this range. The mean is calculated using the day’s climatic data, the standard deviation is the day’s data, and ’n’ is a user-defined input number. For precipitation datasets, the package fills in any missing numbers with an internal format, and R identifies and corrects any aberrant findings, such as precipitation less than zero and outliers.

2.4. Methods

2.4.1. Mann–Kendall Test

The Mann–Kendall (MK) test is a non-parametric statistical method used for trend analysis to determine whether data values are increasing or decreasing over time. A MK Z-value > 1.96 indicates a significantly increasing trend, while a Z-value < −1.96 indicates a significantly decreasing trend at the 5% significance level [38]. The Sen Slope Estimator is a robust, non-parametric method less sensitive to outliers and does not require specific assumptions about data distribution [39]. The Theil–Sen slope estimator is used to determine the magnitude of trends in rainfall and temperature [40,41]. A positive Theil–Sen test statistic indicates an increasing trend, while a negative statistic indicates a decreasing trend. Both tests were conducted using the trend package from R Development climpact-2 software (v1.2) [42].
The slope is then calculated as the median from all slopes:
b = S k
The intercepts are computed for each time step t as:
a t = X t − b ∗ t
On the other hand, the Mann–Kendall test is a nonparametric test for randomness against trend. As for the Sen Estimator, the use of the Mann–Kendall test is well documented in climate extreme analysis [43]. The null hypothesis, H0, is that the data are Independent and Identically Distributed (IID). The alternative hypothesis, HA, is that the data follow a monotonic trend. The test statistics S is defined as:
S = ∑ i = 1 n − 1 ∑ j = i + 1 n s g n X i − X k  
where
s g n ( x ) = 1   i f   x > 0 0   i f   x = 0 − 1   i f   x < 0          
The variance of S is given by:
V s = ( n − 1 ) ( 2 n + 5 ) / 18
The significance of the trend is assessed using a Z value defined as:
Z = S − 1 V s 0 S + 1 V s       i f   S > 0 i f   S = 0 i f   S < 0    
The trend analysis produces the magnitude and relevance of each time series’ trend. If Z is negative and the computed probability above the level of significance, the trend is considered downward. The approach has already been used successfully in comparable investigations [44].

2.4.2. Climate Change Indices

Climate indices, as established globally, are derived from daily maximum and minimum temperatures as well as precipitation data, making them particularly suitable for analyzing extreme weather events [45]. These indices were selected primarily to investigate various aspects of a changing global climate, including shifts in the severity, frequency, and duration of temperature and precipitation extremes.
To perform our investigation, we used R software to construct climate indices, allowing for a thorough examination of climatic extremes [46]. Our study focused on eight historical precipitation indices and eight temperature indices from the 27 core climate change indicators established by the Expert Team on Climate Change Detection and Indices (ETCCDI), (http://etccdi.pacificclimate.org/list_27_indices.shtml, accessed on 25 September 2024). This strategic selection was aimed at evaluating climatic extremes in Ethiopia throughout the designated study period (Table 1).

3. Results and Discussion

3.1. Validation of Satellite Temperature and Rainfall Products

The validation results obtained from the scatter plots between stations and the CHIRPS rainfall dataset revealed correlation coefficient (r = 0.97), the lowest root mean squared error (RMSE = 16.22 mm/month), and an overall bias of 0.90 at α = 0.05 significance level (Figure 2a). This positive correlation indicates that when rainfall readings from ground stations increase, so do the corresponding estimates from the CHIRPS dataset. The CHIRPS dataset effectively captures rainfall patterns, as evidenced by a statistically significant relationship at α = 0.05 level. The study found a strong correlation between CHIRPS rainfall data and observed station data, particularly at lower rainfall values in Ethiopia, indicating the CHIRPS dataset’s exceptional reliability in capturing spatial and temporal rainfall variability across the investigated stations [47]. The CHIRPS dataset, with a low RMSE, accurately replicates recorded rainfall levels, indicating minimal discrepancies between observed and anticipated values, making it a valuable resource for hydrological and climatic research [48]. Although the CHIRPS dataset somewhat underestimates rainfall, this minor error is acceptable and illustrates the dataset’s excellent accuracy for practical applications [49]. These validation findings show that the CHIRPS dataset is strong and accurate at generating reliable rainfall predictions.
The scatter plots comparing station data with the ERA5 monthly mean maximum temperature dataset showed a correlation coefficient of r = 0.84, RMSE of 1.73 °C, and an overall bias of 0.99 (Figure 2b) at α = 0.05 significance level. The correlation coefficient for the monthly mean lowest temperature was r = 0.86, with an RMSE of 1.50 °C and an overall bias of 0.97 (Figure 2c). This positive correlation suggests that when temperature readings from ground stations rise, the estimates from the ERA5 dataset also increase correspondingly. The ERA5 dataset accurately measures temperature fluctuations, as demonstrated by a statistically significant association at α = 0.05 level. These findings support ERA5’s accuracy in depicting temperature patterns, making it an important tool for climate study and historical observation [50]. This strong correlation validates the reliability of CHIRPS and ERA5 datasets were preferred for this research due to their accuracy in representing climate variables across Ethiopia (Figure 2).

3.2. Spatial and Temporal Annual Analysis of the Extreme Temperature

The trend analysis of eight temperature indices across 103 stations from 1994 to 2023 reveals the following percentages, categorized by trend strength and statistical significance at the α = 0.05 level: significant positive trends account for 7.9%, non-significant positive trends comprise 43.2%, significant negative trends represent 9.2%, non-significant negative trends constitute 38.1%, and no trend was observed in 1.6% of the cases, as summarized (Table 2). The analysis of the maximum value of daily maximum temperature (TXx) shows a positive average trend over the study period. However, the annual time series shows that only 14.56% of the observed data had a statistically significant trend, with 13.59% positive and 0.97% negative trends. Furthermore, 83.50% of the stations showed statistically insignificant changes, with 45.63% of them positive and 37.86% negative. In examining spatial distributions of yearly time series for the daily maximum temperature maximum value (TXx), it was found that 59.22% of stations experienced an increase in maximum values. Notably, this included a significant trend of 13.59% in northeastern Ethiopia, whereas the central and north regions of the research area exhibited a non-significant trend of 45.63% (Figure 3a). During the study period, the highest annual mean maximum temperature (TXx) was recorded 35.5 °C in 2005, while the lowest was 32.3 °C in 1996. The annual TXx increased by 0.011 °C per year (Figure 4a), which aligns with findings from other studies [51]. This increase in TXx severe occurrences is mostly caused by oscillations in global sea surface temperatures, which influence meteorological conditions and shape extreme heat events [52].
In contrast, distinct regional patterns emerged in the geographical distribution of the minimum value of the daily maximum temperature (TXn), which tended to fall. Notably, TXn exhibited a non-significant decrease at 51.46% of stations in the northeastern and southeastern regions. Conversely, 47.57% of stations showed relatively minor increases, predominantly in the northern and southwestern areas (Figure 3b). This result is similar to that of a study in the Upper Blue Nile Basin, which identified an increasing trend in TXn, with 69.2% of stations reporting heightened intensity in temperature indices [53]. Because of the diverse local climatic conditions and varying geographical features across Ethiopia, temperature trends exhibit significant regional variations. During the study period, the annual TXn in Ethiopia increased at a rate of 0.01 °C per year. The lowest annual mean TXn was 20.3 °C in 2015, while the highest was 25.7 °C in 2002 (Figure 4b). At most stations, this slight upward trend is not statistically significant and suggests a gradual warming in the minimum values of maximum temperatures.
The highest value of the daily minimum temperature (TNx) found that only 18.45% of the series had a statistically significant trend at α < 0.05, with 17.48% positive and 0.97% negative. In addition, 79.61% of the stations showed a statistically insignificant trend, with 60.19% of these trends positive and 19.42% negative (Table 2). The distribution of the highest daily minimum temperature (TNx) revealed that 77.67% of stations had a positive trend, mainly in the northeast and southeast areas. Notably, 17.48% of stations, including Abala, Aisha, Atsebi, Bure, Debrebirhan, Dire Dawa, Dollooddo, Elidar, Filtu, Harawa, Harghelle, Jijiga, Kelafo, Lmi, Moyale, Omorate Sheble, and Wegletena, showed a significant upward trend (Table S2). This result is consistent with the findings of the study Trends in Rainfall and Temperature Extremes in Ethiopia and Agro-Ecological Zone Levels of Analysis, which found an increase in TNx at 75.7% of the stations examined [54]. In addition, 60.19% of stations in northeastern and southern Ethiopia showed a statistically non-significant increase trend (Figure 3c). This indicates that the warmest nighttime temperatures are rising, indicating an increase in minimum temperatures at night, while both day and night temperatures are rising, with nighttime temperatures increasing at a faster rate [31]. During the study period, the annual TNx in Ethiopia increased at a rate of 0.007 °C per year. The lowest annual mean TNx was 18.5 °C in 1994, 1996, and 1997, while the highest was 20.5 °C in 1998 (Figure 4c). The distribution of TNn shows that 50.49% of stations had a positive, increasing trend. Only 2.91% of stations, including Moyale, Bure, and Aware, showed a substantial rise, while the remaining 47.57%, primarily in central and northeastern Ethiopia, showed nonsignificant increasing trend. In contrast, 46.60% of stations showed a decrease in TNn, with 2.91% significantly decreasing and 43.69% showing a statistically insignificant decline (Figure 3d). This positive TNn value suggested that the lowest temperatures were rising over time, pointing to an overall warming trend [53]. The minimum value of daily minimum temperature (TNn) trended lower by 0.003 °C every year. The lowest annual mean TNn was 9.9 °C in 2017; while the highest was 12.9 °C in 2002 (Figure 4d).
The spatial distribution of cold days (TX10p) revealed positive trends in 63.11% of stations, especially in central, northwest, and southern Ethiopia. Notably, 4.85% of stations, including Arbaminch, Hagermariyam, Jeba, Kibish, and Sherkole, showed a clear increase trend. Additionally, 58.25% of sites in northern, central, and southern Ethiopia showed nonsignificant rise. Meanwhile, 5.83% of stations, including Semera, Jimma, Elidar, Bure, Aisha, and Abala, had a significant downward trend (Figure 3e). These findings indicate that the frequency of extremely hot days per year is increasing, implying that hot extremes are growing faster than cold nights are decreasing [55]. The number of extreme temperature indices, such as TX10p, has decreased by 0.111 days per year. Notably, the lowest annual mean value was 1.09 days in 2004 and the highest was 34.2 days in 1996 (Figure 4e). On the other hand, 90.29% of stations showed a decline in TN10p prevalence across the study period, suggesting a significant change in temperature trends. TN10p significantly decreased at 50.49% of these locations, particularly in central and northeastern Ethiopia. This indicates a significant decrease in the frequency of days with minimum temperatures below the 10th percentile in these areas. A less severe but still discernible decline in TN10p was seen in 39.81% of stations, mostly in the southeast, where non-significant negative trends were recorded (Figure 3f). The overall prevalence of TN10p decreased, indicating a drop in the number of days with minimum temperatures below the 10th percentile, as well as a decrease in severely cold nights, which is consistent with [56]. Quantitatively, the research showed that over the study period, TN10p decreased at an average annual rate of 0.496 days per year. A notable decrease in exceptionally cold nights in recent years is indicated by the lowest annual average of TN10p, which was recorded at 2.6 days in 2023. To demonstrate the amount of change during the research period, the maximum annual average of TN10p was 32.8 days in 1999 (Figure 4f). This finding is consistent with previous studies that indicate a statistically significant negative trend in cold nights (TN10p), with declines ranging from 2.9 to 4.4 days per year [57]. The annual distribution of extreme warm nights (TN90p) found that 88.35% of stations experienced a rise, with warm nights being more common in the southeast. Of them, 66.02% indicated insignificant increases. In contrast, 22.33% of stations, particularly in the Somalia and Afar areas, showed significant upward trends (Figure 3g; Table S2). The TN90p index, which counts the number of nights with minimum temperatures over the 90th percentile, reflects this tendency. It was shown to increase by about 0.232 days every year, peaking at 26.5 days in 2019 (Figure 4g) [58,59]. The increased TN90p frequency suggests more frequent heat extremes and rising nighttime temperatures, as well as warmer nights. These patterns are in line with global climate change observations [60]. The annual distribution of DTR (Diurnal Temperature Range) was decreasing in 85.44% of stations. Of them, 72.82% showed insignificant negative trends in most locations, but 12.62% indicated significant negative trends, mainly in the Oromia and Somalia regions (Figure 3h; Table 2. The findings show that the lowest temperatures are rising faster than the maximum temperatures, resulting in a smaller diurnal temperature range. Previous research has demonstrated that increased cloud cover contributes to this trend by retaining heat at night, resulting in warmer evenings, but also reflecting sunlight during the day, resulting in cooler daytime temperatures [61,62]. Over the period of the study, the Diurnal Temperature Range (DTR) decreased by 0.01 °C per year, with significant drops seen in 1994–1999, 2006, 2010, 2013–2014, and 2016–2023 (Figure 4h). These results are consistent with findings from the study ‘The Long-term Trend in the Diurnal Temperature Range and Its Association with Total Cloud Cover and Rainfall’, which reported that between 1951 and 2014, the global land average diurnal temperature range (DTR) decreased by 0.054 °C per decade [63].

3.3. Spatial and Temporal Annual Analysis of the Extreme Precipitation

The results of annual trend analysis of eight precipitation indices at 103 stations from 1994 to 2023 revealed the following percentages, grouped by trend strength and statistical significance at α = 0.05 level; significant positive trend 6.8%, non-significant positive trend 43.1%, significant negative trend 5.8%, non-significant negative trend 42.6% and no trend 1.7% as summarized (Table 3). The spatial distribution of CDD revealed that 61.17% of stations had a negative, decreasing trend. Notably, 9.71% of stations, including Ambamariam, Assaita, Bati, Cheifra, Combolcha, Geladi, Majete, Mille, and Shahura, showed statistically significant downward trends at the α < 0.05 significance level (Table S3). Meanwhile, the remaining 51.46%, mostly found in northern, central, and southern Ethiopia, exhibited nonsignificant negative trends. In contrast, 36.89% of stations exhibited positive trends, with 32.04% showing insignificant positive trends. Notably, the Shebele and Ziway stations displayed statistically significant positive trends over the study period at a level of α < 0.05 (Figure 5a; Table S3). The analysis of continuous dry days (CDD) showed an insignificant increasing trend of 0.152 days annually. During the study period, the lowest annual mean for CDD was 13 days in 1998, and the highest was 78 days in 2019 (Figure 6a). Conversely, the number of continuous wet days (CWD) has been increasing at a rate of 1.115 days per year. The lowest annual mean for CWD was 53 days in 2009, and the highest was 169 days in 2019 (Figure 6b). This increasing trend in wet days indicates that the frequency and duration of sustained rainfall events are likely to rise [64,65]. Consecutive wet days (CWD) have exhibited notable trends in Ethiopia, with 73.79% of monitoring stations reporting increases. Among them, 16.50% displayed statistically significant positive trends at p < 0.05, primarily in the southeastern area. However, 57.28% showed non-significant positive trends, notably in central and northwest Ethiopia. In contrast, 25.24% of stations showed non-significant negative trends (p < 0.05) (Figure 5b). The increase in CWD combined with a decrease in consecutive dry days (CDD) suggests a probable increase in the number of rainy days across most climatic stations. This tendency indicated more continuous rainfall periods, which might improve water supply and boost agriculture. This pattern shows that dry spells are becoming shorter, while periods of continuous rainfall are becoming more common. This finding aligns with previous studies, which found that consecutive dry days (CDD) decreased while consecutive wet days (CWD) increased [46,66]. The factors contributing to the decrease in consecutive dry days (CDD) and the increase in consecutive wet days (CWD) are atmospheric circulation patterns, the seasonal migration of the Intertropical Convergence Zone (ITCZ), and complex topography [67].
The spatial distribution patterns of annual total precipitation (PRCPTOT) indicated a positive trend in 72.82% of stations, predominantly in the southeast Ethiopia. Among them, 64.08% showed negligible positive trends in the southeast and northwest, whereas 8.74% showed significant upward trends, especially in western Ethiopia (Figure 5c). The positive trend in annual total precipitation (PRCPTOT) indicates a rise in annual rainfall. This upward trend in annual total precipitation indicates rise in overall rainfall, leading to more frequent and intense rainfall events. Over the study period, PRCPTOT increased at a rate of 1.697 mm per year, with the lowest annual average recorded at 763 mm in 2009 and the highest at 1050.3 mm in 2019 (Figure 6c). The increasing trend implies a significant rise in yearly precipitation levels, resulting in more frequent and heavy rainfall events. These findings are congruent with [68], the research title assessment of precipitation extremes and their association with NDVI, monsoon, and oceanic indices. Extreme like annual total precipitation (PRCPTOT) are influenced by factors such as global warming and teleconnection systems, including the El Niño-Southern Oscillation (ENSO) and the positive Indian Ocean Dipole (IOD) [54,69,70].
The annual distribution of RX1 days indicated that 55.34% of stations decreased, mostly in central and northwest Ethiopia. Within this, 48.54% exhibited small decline patterns, especially in central and northern Ethiopia, whereas 6.80% (Aware, Awasharba, Gewane, Gonder, Mahoni, Nazeret, and Quara) showed significant decrease trends at p < 0.05 level (Figure 5d; Table S3). Similarly, the RX5day analysis observed that 56.31% of stations noticed decreasing trends, predominantly in central and northwest Ethiopia. Of them, 48.54% exhibited negligible reduction trends, mostly in northern, northwest, and central Ethiopia, whereas 11.65% indicated statistically significant decreases (Figure 5e; Table 3). The annual time series data revealed that maximum 1-day precipitation (RX1day) increased at an average rate of 0.011 mm. Over the study period, the lowest annual average RX1day was 9.9 mm in 2015, and the highest was 21.5 mm in 2020 (Figure 6d). This increased trend in RX1day indicates that more severe rainfall events occur in a single day, potentially leading to concerns such as flash flooding and urban drainage issues. In contrast, maximum 5-day precipitation (RX5day) declined at an annual rate of 0.011 mm. The lowest annual average RX5day was 37.8 mm in 2015, and the highest was 67.8 mm in 2001. This decrease indicates fewer persistent heavy rainfall episodes lasting five days or more (Figure 6e). Warmer temperatures might increase the atmosphere’s ability to retain moisture, leading to stronger, shorter precipitation episodes (RX1day) and fewer extended heavy rainfall occurrences (RX5day). These findings are similar to those reported in earlier investigations [71,72]. The annual distribution of R10 shows that 54.37% of monitoring stations are experiencing an increase, with 2.91% showing a statistically significant rise at the p < 0.05 level (notably at Debretabor, Neghele, and Sheble), while 51.46% show a non-significant negative trend, most notably in western and southeastern Ethiopia (Figure 5f; Table S3). The time series analysis for R10mm, which represents the number of days with rainfall exceeding 10 mm, found a statistically significant decrease of 0.126 days annually. Throughout the study period, the lowest annual average R10mm was recorded in 2015, when there were no days with rainfall greater than 10 mm. In contrast, the highest annual average R10mm was 30 days in 1998 (Figure 6f). This negative trend indicates a decrease in the frequency of moderate to heavy rainfall events over Ethiopia [73]. The yearly distribution of very wet days (R95p) indicates a downward trend in 66.02% of stations, mainly in central and northwest Ethiopia. Within this category, 56.31% of stations showed a non-significant negative trend, mostly in northern and southern Ethiopia, whereas 9.71% showed a significant negative trend in central Ethiopia (Figure 5g). Moreover, the annual mean precipitation for days with rainfall above the 95th percentile (R95th) decreased by an average of 1.441 mm each year. Throughout the study period, the lowest annual mean R95th was 29.3 mm in 2005 and the highest was 239.9 mm in 1998 (Figure 6g). This indicates a decrease in both the frequency and intensity of extreme rainfall occurrences that exceed the 95th percentile. Similarly, on extremely wet days (R99p), the geographical patterns were similar to those of R95p, with 52.43% of stations indicating a declining trend. Among these, 49.51% showed an insignificant decreasing tendency, whereas 2.91% indicated a significant decreasing trend (Figure 5h; Table 3). The annual mean precipitation on days with rainfall levels above the 99th percentile (R99th) declined at an average rate of 0.9 mm each year. The lowest annual mean total precipitation for R99th was zero mm in both 2004 and 2005, while the highest was 108 mm in 2001 and 2006 (Figure 6h). This decline shows that days with unusually heavy rainfall have become less common over time. These results show that the frequency of very wet days (R95p) and extremely wet days (R99p) is declining over time, indicating a decrease in heavy rainfall events. This trend is consistent with findings from other studies [74,75].

3.4. Variability of Rainfall

The Empirical Orthogonal Functions (EOF) examined spatial and temporal trends of seasonal precipitation from 1994 to 2023. This study identified significant spatial trends and regional variations in precipitation anomalies (Figure 7). The first three modes of Empirical Orthogonal Functions (EOF) account for 59.78% of the variation in JJAS season (Kiremt) precipitation in Ethiopia. The investigation revealed a decline in the seasonal rainfall pattern across Ethiopia. Specifically, the three principal EOF modes accounted for 35.84%, 15.83%, and 8.11% of the variance during JJAS. Most regions in Ethiopia experienced considerable seasonal rainfall variability, with the first mode (EOF1) accounting for 35.84% of the variance, mostly as a positive anomaly. Areas with higher positive loadings showed significant increases in rainfall variability, with these locations observing higher shifts in seasonal rainfall (Figure 7a). This conclusion is congruent with [76], who found that the first empirical orthogonal function of reported rainfall in Ethiopia accounted for 50.6% of total variability The positive anomalies of EOF1 correspond with the decreased pattern shown in PC1. The PC1 time series showed differences in seasonal rainfall during the JJAS season in 1994, 1995, 2001, 2008, 2014, 2015, 2022, and 2023 (Figure 7b). These findings are consistent with earlier research, which has shown that spatial patterns for monthly and seasonal precipitation vary between stations and areas due to differences in climatic dynamics [77,78]. EOF2 revealed negative anomalies over northern Ethiopia, accounting for 15.83% of the overall variance (Figure 7c). This indicated more consistent rainfall amounts throughout time, with fewer extreme and significant variations. The second mode of EOF2 closely follows the general seasonal precipitation pattern, with negative and low loading areas showing less precipitation variability [79]. The third mode of JJAS EOF3 shows a decrease in precipitation (negative anomaly) with an 8.11% variation throughout almost all of Ethiopia (Figure 7e). Rainfall variability in both tropical and extratropical regions is linked to global atmospheric, oceanic conditions and pressure systems interact with the highlands to affect rainfall [80,81].
During the FMAM season, three principal forms of EOF accounted for 45.31%, 15.04%, and 7.44% of the variance. The first mode (EOF1), which accounted for 45.31% of the variation, was predominantly positive and demonstrated significant seasonal rainfall variability throughout the entire country of Ethiopia (Figure 8a). The PC1 time series anomalies throughout the FMAM season show a multi-seasonal decline from 1998 to 2009, followed by an increase from 2010 to 2020 (Figure 8b). This study suggests that FMAM season rainfall in these locations fluctuates dramatically year after year, with considerable precipitation in some years and dry conditions in others, which is consistent with results by [82]. In the southern and southeastern parts of Ethiopia, the second mode (EOF2) accounted for 15.04% of the variance, indicating a negative anomaly and a general drying trend (Figure 8c). The time series anomalies for PC2 throughout the FMAM season exhibit significant seasonal variations, particularly in 1995, 2004, and 2016 (Figure 8d). In the western and southeastern areas, the third mode (EOF3), which accounts for 7.44% of the variance, demonstrated fluctuation with a modest upward anomalous trend (Figure 8e). The principal component analysis for FMAM rainfall (PC3) showed variations in the years 1997, 2001, 2006, 2009, 2017, 2019, and 2023 (Figure 8f). These findings align with a previous study, which proposed different forms of climatic variability as possible drivers of inter-annual and intra-seasonal variability in East African rainfall [80].

3.5. Implications for Adaptation and Mitigation Policies

The study’s findings highlight considerable increases in temperature and precipitation extremes in Ethiopia, which will have important implications for adaptation and mitigation initiatives directed at vulnerable communities. As climate change continues to influence weather patterns, it is vital to incorporate these results into policy frameworks in order to improve resilience and reduce vulnerability. In this context, we propose the following recommendations:
  • The study’s findings will aid Ethiopia’s National Adaptation Plan (NAP-ETH) in increasing climate resilience and adaptation capabilities. Policymakers ought to emphasize efforts to address critical climate problems by integrating extreme weather patterns with the NAP’s strategic goals in agriculture, water management, and healthcare.
  • This study underlines the need for early warning systems for managing extreme weather events, as well as the necessity of community preparedness and disaster risk management systems, which are congruent with Ethiopia’s Climate Resilient Green Economy program.
  • This study highlighted the need for active community participation in effective adaptation, as well as community-based efforts that allow local stakeholders to adopt context-specific solutions through training programs and capacity-building activities.
  • Finally, the paper proposes areas for future research to better understand the complexities of climate impacts on vulnerable populations. Continuous monitoring and evaluation of climate changes will be essential for refining adaptation strategies and guaranteeing their relevance in a fast-changing environment.

4. Conclusions

In conclusion, climate change remains the most important global worry of the twenty-first century, with significant increases in severe precipitation and temperature events around the world. This may have significant consequences for several sectors, including agriculture, water resources, health, and the economy. The normalized seasonal rainfall anomaly from the empirical orthogonal function (EOF) analysis revealed a reduction in rainfall throughout the JJAS (Kiremt) season, particularly in north western, central and western Ethiopia. The decrease is evident in the first EOF mode, which accounts for 35.84% of precipitation variability. Meanwhile, throughout the FMAM (Belg) season, the first EOF mode, which accounts for 45.31% of the variability, frequently produces negative values. This indicates high fluctuation, implying irregular changes that might affect regional water supplies and agricultural planning.
The analysis reveals that absolute extreme temperatures, including the number of hottest days and warmest nights, have risen over the last three decades, while the number of coldest days has decreased. This tendency is consistent with global warming patterns, implying that rising greenhouse gas concentrations may alter Ethiopia’s climate. Conversely, some essential precipitation indexes have seen considerable increases in trends. Total annual precipitation (PRCPTOT) has increased at a rate of 1.7 mm per year, revealing that total rainfall is increasing. Furthermore, maximum daily precipitation (Rx1day) has increased by 0.01 mm per year, indicating that while average daily rainfall is not increasing substantially, there are minor increases in the most intense rainfall events. In addition, the number of continuous wet days (CWD) has increased by 1.12 days annually, suggesting that there are more days with prolonged rainfall. These data may indicate a shift toward wetter weather in the region. Evaluating changes in temperature and precipitation extremes under warming climates reveals considerable negative consequences for Ethiopia’s natural environment and socioeconomic activity. These shifts in rainfall and temperature patterns, may be caused by climate change and weather system variability, and have a significant impact on various sectors, including water supplies, agriculture, infrastructure, health, and the environment.
Understanding shifting precipitation and temperature trends, improving regional decision-making, adapting to climatic realities, improving drainage systems, and creating strategic settlement plans are all critical steps. In addition, adopting proper adaptation and mitigation measures is crucial for lowering the effects of extreme climatic occurrences in Ethiopia. The findings of this study are critical for providing reliable climate information, identifying and attributing climate extremes, and improving present and future conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos16020133/s1. Table S1: Descriptions of the studied meteorological stations for Map of Ethiopia Highlighting 103 Meteorological Stations Elevation (m); Table S2: Annual trends of extreme temperature for 103 stations for Spatial and Temporal Annual Analysis of the Extreme Temperature; Table S3: Annual trends of extreme precipitation for 103 stations for Spatial and Temporal Annual Analysis of the Extreme Precipitation

Author Contributions

E.A.M. contributed to the conceptualization, methodology, formal analysis, investigation, writing of the original draft, and reviewing and editing. K.A.A. was responsible for visualization, and reviewing and editing. X.Z. played a role in conceptualization, methodology, formal analysis, investigation, as well as funding acquisition and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (2017YFC1502000).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Some data available in a publicly accessible with, links, while observational data cannot be shared to third party because of data policy.

Acknowledgments

I would like to express my gratitude to everyone who has contributed to my research and supported me in some way over this fantastic trip. First and foremost, I am grateful to Xiefei Zhi, my supervisor, for his support and all of the helpful conversations and brainstorming sessions, especially during the challenging conceptual development period. His keen ideas came in handy at numerous points during my investigation. I am grateful to Candidate Kemal Adem for his insightful insights and ideas. I value his willingness to meet with me at any time and to take part in several of my research experiments. Despite his busy schedule, he still pushes me to participate in a variety of activities and helps me when I am in trouble, which continues to impress me. Special thanks to Nanjing University of Information Science and Technology School of Atmospheric Sciences and Ministry of Finance and Commerce of China (MOFCOM) for their financial support, as well as the Ethiopian Meteorology Institute (EMI) for allowing me to complete my master’s degree and study in China.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of Ethiopia Highlighting 103 Meteorological Stations Elevation (m).
Figure 1. Map of Ethiopia Highlighting 103 Meteorological Stations Elevation (m).
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Figure 2. Shows scatter plots for data validation between observed values (ground measurements) and satellite datasets for (a) rainfall, (b) maximum temperature and (c) minimum temperature on a monthly time scale in Ethiopia from 1998 to 2020.
Figure 2. Shows scatter plots for data validation between observed values (ground measurements) and satellite datasets for (a) rainfall, (b) maximum temperature and (c) minimum temperature on a monthly time scale in Ethiopia from 1998 to 2020.
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Figure 3. Spatial patterns of severe temperature indices in Ethiopia from 1994 to 2023 include (a) TXx, (b) TXn, (c) TNx, (d) TNn, (e) TX10p, (f) TN10p, (g) TN90p, and (h) DTR. Upward green and black triangles indicate significant and non-significant increasing trends, respectively, while downward blue and red triangles represent significant and non-significant decreasing trends. Yellow circles indicate no trend. ‘Sig’ and ‘No Sig’ denote significant and non-significant trends.
Figure 3. Spatial patterns of severe temperature indices in Ethiopia from 1994 to 2023 include (a) TXx, (b) TXn, (c) TNx, (d) TNn, (e) TX10p, (f) TN10p, (g) TN90p, and (h) DTR. Upward green and black triangles indicate significant and non-significant increasing trends, respectively, while downward blue and red triangles represent significant and non-significant decreasing trends. Yellow circles indicate no trend. ‘Sig’ and ‘No Sig’ denote significant and non-significant trends.
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Figure 4. This time series displays the annual average trends of severe temperature indicators in Ethiopia, including (a) TXx, (b) TXn, (c) TNx, (d) TNn, (e) TX10p, (f) TN10p, (g) TN90p, and (h) DTR over time.
Figure 4. This time series displays the annual average trends of severe temperature indicators in Ethiopia, including (a) TXx, (b) TXn, (c) TNx, (d) TNn, (e) TX10p, (f) TN10p, (g) TN90p, and (h) DTR over time.
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Figure 5. Spatial patterns of severe temperature indices in Ethiopia from 1994 to 2023, including (a) CDD, (b) CWD, (c) PRCPTOT, (d) RX1day, (e) RX5day, (f) R10mm, (g) R95p and (h) R99p. Upward green and black triangles indicate significant and non-significant increasing trends, respectively, while downward blue and red triangles represent significant and non-significant decreasing trends. Yellow circles indicate no trend. ‘Sig’ and ‘No Sig’ denote significant and non-significant trends.
Figure 5. Spatial patterns of severe temperature indices in Ethiopia from 1994 to 2023, including (a) CDD, (b) CWD, (c) PRCPTOT, (d) RX1day, (e) RX5day, (f) R10mm, (g) R95p and (h) R99p. Upward green and black triangles indicate significant and non-significant increasing trends, respectively, while downward blue and red triangles represent significant and non-significant decreasing trends. Yellow circles indicate no trend. ‘Sig’ and ‘No Sig’ denote significant and non-significant trends.
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Figure 6. This time series displays the annual average trends of severe precipitation indicators in Ethiopia, including (a) CDD, (b) CWD, (c) PRCPTOT, (d) RX1day, (e) RX5day, (f) R10mm, (g) R95p and (h) R99p over time.
Figure 6. This time series displays the annual average trends of severe precipitation indicators in Ethiopia, including (a) CDD, (b) CWD, (c) PRCPTOT, (d) RX1day, (e) RX5day, (f) R10mm, (g) R95p and (h) R99p over time.
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Figure 7. This displays the three key modes of empirical orthogonal functions (EOFs) and principal component analyses (PCAs) for seasonal precipitation during the JJAS (a–f) periods. (The red color represents positive anomalies, while blue indicates negative anomalies).
Figure 7. This displays the three key modes of empirical orthogonal functions (EOFs) and principal component analyses (PCAs) for seasonal precipitation during the JJAS (a–f) periods. (The red color represents positive anomalies, while blue indicates negative anomalies).
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Figure 8. This displays the three key modes of empirical orthogonal functions (EOFs) and principal component analyses (PCAs) for seasonal precipitation during the FMAM (a–f) periods. (The red color represents positive anomalies, while blue indicates negative anomalies).
Figure 8. This displays the three key modes of empirical orthogonal functions (EOFs) and principal component analyses (PCAs) for seasonal precipitation during the FMAM (a–f) periods. (The red color represents positive anomalies, while blue indicates negative anomalies).
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Table 1. List of extreme temperature and precipitation indices used in this study recommended by the ETCCDI.
Table 1. List of extreme temperature and precipitation indices used in this study recommended by the ETCCDI.
CodeDescriptionDefinitionsUnits
Temperature
TXxWarmest/Hottest daysAnnual maximum value of daily maximum temperature°C
TNxWarmest nightsAnnual maximum value of daily minimum temperature°C
TXnColdest daysAnnual maximum value of daily maximum temperature°C
TNnColdest nightsAnnual minimum value of daily minimum temperature°C
Tn10pCold nightsPercentage of days when Tn < 10th percentile%
Tx10pCold daysPercentage of days when Tx < 10th percentile%
Tn90pWarm nightsPercentage of days when Tn > 90th percentile%
DTRDiurnal temperature rangeAnnual mean difference between Tx and Tn.°C
Precipitation
PRCPTOTAnnual total precipitationAnnual total precipitation in wet days (pr > 1 mm)mm
Rx1dayMax 1-day precipitationAnnual maximum 1-day precipitationmm
Rx5dayMax 5-day precipitationAnnual maximum consecutive 5-day precipitationmm
R10Number of heavy
precipitation days		Annual count of days when pr ≥ 10 mmDays
CDDConsecutive dry daysAnnual maximum number of consecutive days with pr < 1 mmDays
CWDConsecutive wet daysAnnual maximum number of consecutive days with pr ≥ 1 mmDays
R95pVery wet daysTotal precipitation when pr > 95th percentilemm
R99pExtremely wet daysTotal precipitation when pr > 99th percentilemm
Table 2. The annual trends of extreme temperature indices for Ethiopia from 1994 to 2023 are presented, with “Sig.” indicating significant trends and “No Sig.” indicating non-significant trends.
Table 2. The annual trends of extreme temperature indices for Ethiopia from 1994 to 2023 are presented, with “Sig.” indicating significant trends and “No Sig.” indicating non-significant trends.
Positive TrendNegative TrendNo TrendBoth Positive and
Negative Trend
IndicesTotal
(%)		Sig.
(%)		Non Sig. (%)Total
(%)		Sig.
(%)		Non Sig. (%)Total
(%)		Total Sig. (%)Total Non
Sig. (%)
TXx59.2213.5945.6338.830.9737.861.9414.5683.50
TXn47.570.0047.5751.460.0051.460.970.0099.03
TNx77.6717.4860.1920.390.9719.421.9418.4579.61
TNn50.492.9147.5746.602.9143.692.915.8391.26
TX10p63.114.8558.2534.955.8329.131.9410.6887.38
TN10p8.740.008.7490.2950.4939.810.9750.4948.54
TN90p88.3522.3366.0210.680.0010.680.9722.3376.70
DTR13.591.9411.6585.4412.6272.820.9714.5684.47
Table 3. The annual trends of extreme precipitation indices for Ethiopia from 1994 to 2023 are presented, with “Sig.” indicating significant trends and “No Sig.” indicating non-significant trends.
Table 3. The annual trends of extreme precipitation indices for Ethiopia from 1994 to 2023 are presented, with “Sig.” indicating significant trends and “No Sig.” indicating non-significant trends.
Positive TrendNegative TrendNo TrendBoth Positive and
Negative Trend
IndicesTotal
(%)		Sig.
(%)		Non Sig.(%)Total
(%)		Sig.
(%)		Non Sig. (%)Total
(%)		Total
Sig. (%)		Total Non
Sig. (%)
PRCPTOT72.828.7464.0826.210.9725.240.979.7189.32
R10mm54.372.9151.4644.664.8539.810.977.7791.26
RX1day42.723.8838.8355.346.8048.541.9410.6887.38
RX5day42.725.8336.8956.3111.6544.660.9717.4881.55
CDD36.894.8532.0461.179.7151.461.9414.5683.50
CWD73.7916.5057.2825.240.0025.240.9716.5082.52
R95p33.015.8327.1866.029.7156.310.9715.5383.50
R99p42.725.8336.8952.432.9149.514.858.7486.41
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Mohammed, E.A.; Zhi, X.; Abdela, K.A. Extreme Weather Patterns in Ethiopia: Analyzing Extreme Temperature and Precipitation Variability. Atmosphere 2025, 16, 133. https://doi.org/10.3390/atmos16020133

AMA Style

Mohammed EA, Zhi X, Abdela KA. Extreme Weather Patterns in Ethiopia: Analyzing Extreme Temperature and Precipitation Variability. Atmosphere. 2025; 16(2):133. https://doi.org/10.3390/atmos16020133

Chicago/Turabian Style

Mohammed, Endris Ali, Xiefei Zhi, and Kemal Adem Abdela. 2025. "Extreme Weather Patterns in Ethiopia: Analyzing Extreme Temperature and Precipitation Variability" Atmosphere 16, no. 2: 133. https://doi.org/10.3390/atmos16020133

APA Style

Mohammed, E. A., Zhi, X., & Abdela, K. A. (2025). Extreme Weather Patterns in Ethiopia: Analyzing Extreme Temperature and Precipitation Variability. Atmosphere, 16(2), 133. https://doi.org/10.3390/atmos16020133

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