Rainfall Variability Research Articles

Routine Climate Monitoring in the State of Hawai‘i: Establishment of State Climate Divisions

Abstract The Hawaiian Islands have some of the most spatially diverse rainfall patterns on Earth, affected by prevailing trade winds, midlatitude disturbances, tropical cyclones, and complex island topography. However, it is the only state in the United States that does not have assigned climate divisions (boundaries defining climatically homogeneous areas), which excludes it from many national climate analyses. This study establishes, for the first time, official climate divisions for the state of Hawai‘i using cluster analysis applied to monthly gridded rainfall data from 1990 to 2019. Twelve climate divisions have been identified: two divisions were found each for the islands of Kaua‘i (Leeward Kaua‘i and Windward Kaua‘i), O‘ahu (Waianae and Ko‘olau), and Maui County (Leeward Maui Nui and Windward Maui Nui), and six divisions were identified for Hawai‘i Island (Leeward Kohala, Windward Kohala, Kona, Hawai‘i Mauka, Ka‘u, and Hilo). The climate divisions were validated using a statewide area-weighted division-average rainfall index which successfully captured the annual cycle and interannual rainfall variations in the statewide average rainfall series. Distinct rainfall seasonality features and interannual/decadal variability are found among the different divisions; Leeward Maui Nui, Leeward Kaua‘i, Kona, and Hawai‘i Mauka displayed the most significant rainfall seasonality. The western Hawai‘i Island divisions show the most significant long-term decreasing trends in annual rainfall during the past 100 years (ranging from −2.5% to −5.0% per decade). With these climate divisions now available, Hawai‘i will have access to numerous operational climate analyses that will greatly improve climatic research, monitoring, education, and outreach, as well as forecasting applications.

Ocean Surface Warming and Long-Term Variability in Rainfall in Equatorial Pacific Atolls

Ocean Surface Warming and Long-Term Variability in Rainfall in Equatorial Pacific Atolls

Evaluation of Temperature and Rainfall Variation in Yola, Adamawa State, Nigeria

Evaluation of Temperature and Rainfall Variation in Yola, Adamawa State, Nigeria

A multi-proxy lacustrine sedimentary record of sub-decadal to decadal scale variability of monsoon during the late Holocene in southern India

The present study is aimed at understanding the sub-decadal to decadal scale variability of rainfall in southern India based on a sedimentary record (dated using AMS 14C, 210Pb, 137Cs methods) from Madagadakere lake (MK) situated in the foothills of the Western Ghats. A strong linkage between sediment magnetic signature and the instrumental rainfall record for the past 115 years demonstrates that the environmental magnetic proxies are useful and have greater applicability in the paleomonsoonal reconstructions. Fluctuations in the mineral magnetic, grain size, and geochemical parameters of the studied sediment profile revealed that pedogenic activity and terrigenous sediment flux to the lake varied cyclically during the Late Holocene period in response to varying intensities of rainfall. The region experienced higher or increasing trend of rainfall during periods 2100–1900, 1600–1250, 1050–925, 680–600, and 350–270 cal. years B.P., with low rainfall/decreasing trend during intervening periods. The spectral analysis of magnetic parameters revealed significant periodicities of 291, 164, 101, 54, 48, 44, 41, 39, 105, 48, and 41 years, which are very well documented in other paleoclimatic records, and whose origin is ascribed unequivocally to variation in Total Solar Irradiance (TSI). We propose that the periods of higher values for concentration-dependent magnetic parameters (higher rainfall) can be attributed to the timings of the increased TSI, positive mode of Indian Ocean Dipole (IOD), northward migration of Intertropical convergence zone (ITCZ) and weakened El-Niño southern oscillation (ENSO) activity in the region, establishing the teleconnection between different climatic systems.

Exploring the Epidemiological Surveillance of Hepatitis A in South Africa: A 2023 Perspective.

Hepatitis A (HAV) presents a significant global health concern with diverse clinical manifestations primarily transmitted through fecal-oral routes, emphasizing the critical role of sanitation and water cleanliness in transmission dynamics. Age-related variations, notably asymptomatic presentation in children, add complexity. The World Health Organization's (WHO) endemicity classification aids in understanding prevalence and control strategies. This study examines 2023 South African epidemiological data on HAV cases, evaluating age distribution, incidence rates, and provincial disparities. Data from the national surveillance system and weather services were analyzed. Findings reveal distinct age-related trends, with the highest seroprevalence observed in the 5-9 age group with the most burdened areas located in the Western Cape, KwaZulu-Natal, and Gauteng provinces. Furthermore, seasonal rainfall variations correlate with increased incidence in Western Cape and KZN. The amalgamation of results suggest a potential epidemiological shift, emphasizing the need for updated immunization strategies. Noteworthy patterns, like the rise in 5-9-year-olds, may be influenced by factors such as school clustering and migration. Provincial disparities and the impact of climatic events underscore the necessity for dynamic vaccination strategies and surveillance network enhancements. This study highlights the urgency for improved understanding and response to HAV in South Africa.

Impacts of Rainfall Variability on Streamflow in the Drylands of Northern Kenya: Assessing Water Availability under a Changing Climate

Water availability is a function of climatic and land surface conditions, which determine the amount and distribution of atmospheric water as it reaches the surface. This largely depends on rainfall, whose variability affects water, food and livelihood security. This paper sought to quantify the effects of rainfall variability on water availability in an effort to support effective water resources management. Coefficient of Variation (CV), Standardized Anomaly Index (SAI) and Mann-Kendall trend test approaches were used to assess variability and trends, while correlation and regression analysis were employed to determine effects of rainfall variability on streamflow. A hydrological model, Soil and Water Assessment Tool (SWAT), was used to simulate streamflow with a view to assessing water availability under two climate change scenarios; Representative Concentration Pathways (RCP) 4.5 and 8.5. Results show that the area experiences moderate to extreme rainfall variability, as indicated by CV and SAI values that ranged from 20 to 99% and -2.5 to +3 respectively, resulting in moderate to extreme floods and droughts that often disrupt livelihoods. Current streamflow simulations (1981 – 2020) indicated increasing trends. Near and far future streamflow volumes will decrease by 15% during the April-May-June season while during October-November-December season they will increase by 13%, compared to the present. Increasing trends of rainfall and streamflow indicate that the area has opportunities for rainwater harvesting while the high variability indicates a need for early warning systems to cushion communities from climatic shocks evidenced by the impacts of climate extremes experienced in the area.

The role of commercial agriculture in meeting sustainable development goals in South Africa: Evidence from municipal-level total factor productivity analysis

Improving agricultural productivity is critical, both locally and globally, particularly in the pursuit of the Sustainable Development Goals (SDGs) of eradicating poverty (SDG 1), increasing food production for food security (SDG 2), and promoting efficient use of agricultural resources and sustainable farming practices (SDG 12). This study examines and compares total factor productivity efficiency (TFPE) in the South African commercial agricultural sector, along with its drivers, using local municipal-level data. We used the Färe-Primont index to assess municipalities' TFPE levels, considering both traditional (economic) factors (labour, land, and operating expenses) and environmental variables (temperature, rainfall, and soil moisture index). Our TFPE index results are not only lower, but they show more variation across municipalities than the economic TFPE index, which is commonly used in agricultural productivity research. This implies that ignoring environmental variables may bias analysis; therefore, future studies should consider including environmental factors in their analyses. We then used hierarchical clustering to group municipalities with similar TFPE levels and components, followed by fractional regression to identify the drivers of efficiency levels. The cluster analysis results reveal that the best-performing municipalities are in cluster 4, comprising municipalities from the Western Cape Winelands specialising in horticulture production, municipalities near urban areas with better market proximity, those engaged in the global value chain, and those with a high proportion of farmers who own most of their farmland. Furthermore, the fractional regression results show that age, gender, race, market proximity, land use types, and production diversification are significant drivers of TFPE and its components. Our study offers insights into the divergent agricultural TFPE performance among municipalities and the underlying factors causing these disparities. The findings can inform the development of targeted strategies, particularly at the local level, aimed at enhancing agricultural productivity and making significant contributions to achieving the SDGs.

In-depth exploration of precipitation dynamics in the Wadi El Bey watershed (North-eastern Tunisia): comprehensive study of rainfall variability and its relationship with teleconnection patterns

In-depth exploration of precipitation dynamics in the Wadi El Bey watershed (North-eastern Tunisia): comprehensive study of rainfall variability and its relationship with teleconnection patterns

Improving estimates of water resources availability over North Tropical South America: comparison of two satellite precipitation merging schemes

Low-density precipitation measurements impair the ability of hydrological models to estimate surface water resources accurately. Remote sensing techniques and climate models can help to improve the estimation of the space-time rainfall variability. However, they alone are not good enough to be used in surface models built to support water management. In this research, we test the improvement of rainfall field estimation by using hydrological modelling based on the premise that a higher hydrological performance generally implies that precipitation is more consistent with streamflow observations and evaporation estimates in the basin. The SWAT model was forced with two satellite and rain gauge blending techniques and with the traditional IDW deterministic interpolation method from stations. The three simulated streamflows were compared separately against observed records. We do not only focus the comparison on one hydrological performance metric but also conduct a deeper evaluation using several hydrological signatures and statistics. We included the bias, the temporal correlation, the relation of general variability, and an analysis of the Flow Duration Curves (we found that low and medium segments were estimated correctly, whereas the high segments were underestimated). We conclude that either combination technique has its advantages over the other and that both outperform the performance achieved by the IDW in most of the defined criteria, with an overall 10% improvement and with individual streamflow gauge performance enhancement up to 50%.

A GIS-based Approach to Spatially Explicit Mean Monthly Areal Rainfall Estimation of Krishna District, Andhra Pradesh, India

Rainfall analysis is pivotal for understanding hydrological systems, crucial for effective water resource management. In this study spatial rainfall analysis is carried out for Krishna District of Andhra Pradesh using ArcGIS software. Rainfall data for a period of 30 years (1992-2021) was collected from the Directorate of Economics and Statistics, Vijayawada, Andhra Pradesh. Utilizing the spatial interpolation techniques in Geographic Information System (GIS) software, mean monthly areal rainfall was found using the three methods viz., Arithmetic mean, Thiessen’s polygon and isohyet method. This study utilized Geographic Information Systems (GIS) to visualize spatial patterns of rainfall across Krishna district. To account for missing data in the rainfall record, the normal ratio method was implemented. Spatial maps were generated to depict mean monthly areal rainfall variations. The analysis yielded mean monthly areal rainfall for Krishna district as 84.72 mm, 85.22 mm and 83.97 mm using the arithmetic mean, Thiessen polygon and Isohyet methods, respectively. Isohyet method is considered to be more accurate and rainfall is the basic data required for all hydrological analysis as an input layer for runoff generation, water resources planning, evapotranspiration studies, crop planning, flood estimation and water budget estimations etc.

Deciphering of the morpho-physiological traits of two native grasses from Argentina with contrasting drought resistance strategies

Context Semiarid environments, such as the Patagonian shrublands, are characterised by having shrubby patches surrounded by grasses with different ecophysiological strategies to tolerate long dry periods. Aims We hypothesised that coexisting grasses of the Patagonian rangeland, already classified as drought-escaping or drought-tolerant, have different traits according to the season and the annual rainfall events. Methods Two dominant native grasses were selected: Pappostipa speciosa (evergreen) and Poa ligularis (deciduous). Samples were collected in the four seasons for the term of 1 year. Rainfall events and soil water content of each season were determined. Spring was the wettest season and autumn the driest. Physiological (relative water content, pigments, hormones), biochemical (polyphenols, antioxidant activity) and morphological traits were measured in the four seasons. Key results P. speciosa was characterised by keeping evergreen leaves with high production of polyphenols as secondary metabolites with high antioxidant capacity during the dry autumn and winter seasons. P. ligularis was characterised by high contents of gibberellin (GA1), auxin (IAA), total phenols, total flavonoids and tartaric acid esters, and high antioxidant capacity in roots during the autumn dry season. In addition, P. ligularis leaves had higher content of carotenoids and polyphenols than P. speciosa during the summer dry season. Conclusions The major adaptive strategy to tolerate dry periods is the high activity of the secondary metabolism, mainly in leaves in P. speciosa (a drought-tolerant grass) and in roots in P. ligularis (a drought-escaping grass). Implications Rainfall variations during a year can affect the phenological growth stages and the metabolism of two native grasses from Argentina characterised by different drought resistance mechanisms.

Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia.

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana sub-basin (LTSB) of Ethiopia's upper Blue Nile basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices are evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). The researcher used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% consecutive wet days (CWD), 13.33% (R95p and R99p), and 6.66% highest rainfall amount in a 1-day period (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of number of heavy precipitation days (R10mm), annual total wet day rainfall (PRCPTOT), and consecutive dry days (CDD), with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations location. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

The trends and spatiotemporal variability of temperature and rainfall in Hulbarag district, Silte Zone, Ethiopia

Ethiopia gets its agricultural water primarily from rainfall. This study was intended to investigate current climate variability and trends across space and time. Daily gridded temperature and rainfall data from 1993 to 2022 in the Hulbarag district, Silte Zone of Ethiopia was obtained from the Ethiopian National Metrological Institute and the Climate Hazard Group Infrared Precipitation with Station. The trends and variability of temperature and rainfall were analyzed using the Mann-Kendall trend test, Sen's slope, coefficient of variation, precipitation concentration index, and rainfall anomaly index. The results indicated that annual, spring, and summer rainfall revealed statistically significant decreasing trends at Hulbarag and Sankura stations, with the magnitude of −13.4, −11.6, and −10.6 mm per year and −6.8, −3.6 and −10.9 mm per year respectively. Conversely, autumn and winter season rainfall showed statistically significant increasing trends at Hulbarag and Sankura stations, with the magnitude of 5.1 and 5.5mm per year and 3.4 and 1.84mm per year consecutively. Between 43 % and 47 % of the observation periods had negative anomalies. The average yearly temperature, average yearly minimum temperature, and average yearly maximum temperatures at Sankura and Fonko stations all displayed statistically significant increasing trends, with a magnitude of 0.091 °C, 0.009 °C and 0.051 °C per year and 0.03 °C,0.01 °C and 0.0022 °C per year successively. It is advisable to develop a farming system that is climate-resilient by improving the adaptive capacity of wheat and maize-growing farmers by expanding the availability of early maturing seeds, changing crop calendars, and enhancing proactive and credible climate information services.

Spatiotemporal climate variability and extremes in Middle Awash Afar region Ethiopia: implications to pastoralists and agro-pastoralists food security

PurposeThis study aims to examine the phenomenon of climate variability and its implications for pastoralists and agro-pastoralists food security in Amibara and Awash Fentale districts of the Afar region, Ethiopia.Design/methodology/approachThe study relied on meteorological records of temperature and rainfall in the study area between 1988 and 2018. Besides, literature on the topic was reviewed to make caveats on the literal picture that comes from quantitative data, and that is the contribution of this study to the existing debate on climate change and variability. The spatiotemporal trend was determined using the Mann–Kendall test and Sen’s slope estimator, while variability was analyzed using the coefficient of variation and standardized anomaly index, and standardized precipitation index/standardized precipitation evapotranspiration index were applied to determine the drought frequency and severity.FindingsThe result reveals that the mean seasonal rainfall varies from 111.34 mm to 518.74 mm. Although the maximum and minimum rainfall occurred in the summer and winter seasons, respectively, there has been a decrease in seasonal and annual at the rate of 2.51 mm per season and 4.12 mm per year, respectively. The study sites have been experiencing highly seasonal rainfall variability. The drought analysis result confirms that a total of nine agricultural droughts ranging from moderate to extreme years were observed. Overall, the seasonal and annual rainfall of the Amibara and Awash Fentale districts showed a decreasing trend with highly temporal variations of rainfall and ever-rising temperatures, and frequent drought events means the climate situation of the area could adversely affect pastoral and agro-pastoral households’ food security. However, analysis of data from secondary sources reveals that analyzing precipitation just based on the meteorological records of the study area would be misleading. That explains why flooding, rather than drought, is becoming the main source of catastrophe to pastoral and agro-pastoral livelihoods.Practical implicationsThe analysis of temperature and rainfall dynamics in the Afar region, hence the inception of all development interventions, must take the hydrological impact of the neighboring regions which appears to be useful direction to future researchers.Originality/valueThe research is originally conducted using meteorological and existing literature, and hence, it is original. In this research, we utilized a standardized and appropriate methodology, resulting in insights that augment the existing body of knowledge within the field. These insights serve to advance scholarly discourse on the subject matter.

Assessment of the Impact of Magnesium and Nitrogen Fertilization on Two Species of Grasses Used as Horse Feed

The aim of this study was to determine the effect of nitrogen (three doses of N) and magnesium (two doses of Mg) fertilization on the yield and quality of fodder obtained from two old and extensive grass species Festulolium braunii cv. ‘Felopa’ and Lolium multiflorum cv. ‘Tur’ in field cultivation under dry conditions. F. braunii was better adapted to cultivation on light, dry soils than L. multiflorum; in such conditions, it produces higher yields of dry matter and protein, characterized by a higher concentration of nutrients. F. braunii fertilized with doses of 120 and 180 kg N∙ha−1 yielded higher than that fertilized with a dose of 60 kg N∙ha−1, and L. multiflorum produced similar yields after applying doses of 60, 120 and 180 kg N∙ha−1. For tested grass pasture, a single N application after the start of vegetation in two forms (fast- and slow-acting) appears to be adequate. Resignation from splitting the nitrogen dose due to variable rainfall distribution that can occur after the first cut during the dry summer is beneficial due to a reduction in the losses of nutrients and environmental burdens.

Analyzing the impact of ocean-atmosphere teleconnections on rainfall variability in the Brazilian Legal Amazon via the Rainfall Anomaly Index (RAI)

Drought events are mainly associated with alterations in ocean-atmosphere patterns, leading to abnormally dry conditions due to reduced precipitation. This study aims to enhance the understanding of the spatiotemporal relationship of atmospheric teleconnections associated with droughts in the Brazilian Legal Amazon (BLA), based on the Rainfall Anomaly Index (RAI), utilizing 1675 precipitation grid points from the Global Precipitation Climatology Centre (GPCC) for the period 1970–2019. The results revealed that Principal Component Analysis (PCA) and cluster analysis of RAI demonstrated significant climatic variability, with 15 components explaining 68% of the total variance and identifying two homogeneous regions closely aligning with observed conditions. Atmospheric teleconnections showed substantial variation in spatial and temporal extent in extreme drought events in the years 2005, 2010, 2015–2016, and 2019 in the BLA. The findings indicate that on an interannual scale, the dominant teleconnections in the BLA are the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Tropical North Atlantic (TNA), Pacific North American (PNA) pattern, and Atlantic Meridional Overturning Circulation (AMOC). In contrast, on a decadal scale, the Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), Oceanic Niño Index (ONI), and Tropical South Atlantic (TSA) prevail. Our results provide valuable insights into the nature of drought events in the Amazon, including strategies for the early detection of their intensity and affected areas, demonstrating that atmospheric teleconnections significantly influence both interannual and decadal periods. These influences are concentrated on specific temporal scales and during particular years. Conclusively, this study underscores the critical role of atmospheric teleconnections in shaping drought dynamics in the BLA, offering a foundation for predictive models and mitigation strategies to combat climate-induced challenges in the region.

Optimization of CMIP6 models for simulation of summer monsoon rainfall over India by analysis of variance

AbstractThe advent of weather and climate models has equipped us to forecast or project monsoon rainfall patterns over various spatiotemporal scales; however, utilizing a single model is not usually sufficient to yield accurate projection due to the inherent uncertainties associated with the individual models. An ensemble of models or model runs is often used for better projections as a multimodel ensemble (MME). This study analyzes the accuracy of MME in simulating the Indian summer monsoon rainfall (ISMR) variability using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations. The results highlighted that although the MME primarily reproduces the observed pattern and annual cycle of rainfall, significant biases are noted over homogeneous meteorological regions of India, except northeast India. To overcome this issue, an analysis of variance (ANOVA) and post hoc statistical tests are employed to identify a group of models for which the modified MME gives a better estimate of rainfall and reduces the bias significantly. Our findings underscore the potential of ANOVA and post hoc tests as a practical approach to enhancing the accuracy of multimodel ensemble rainfall for the assessment of model projections.

CON-SST-RAIN: Continuous Stochastic Space–Time Rainfall generation based on Markov chains and transposition of weather radar data

In this study, we present CON-SST-RAIN, a novel stochastic space–time rainfall generator specialized for model-based urban drainage design and planning. CON-SST-RAIN is based on Markov Chains for sequences of dry/rainy days and uses stochastic storm transposition (SST) to generate realistic rainfall fields from weather radar data. CON-SST-RAIN generates continuous areal rainfall time series at arbitrary lengths. We propose a method for updating the Markov Chains by each passing year to better incorporate low-frequency variation in inter-annual rainfall values. The performance of CON-SST-RAIN is tested against multi-year records from rain gauges at both point and catchment scales. We find that updating the Markov Chains has a significant impact on the inter-annual variation of rainfall, but has little effect on mean annual/seasonal precipitation and dry/wet spell lengths. CON-SST-RAIN shows good preservation of extreme rain rates (including sub-hourly values) compared to observed rain gauge data and the original SST framework.

Evaluation of multi-source satellite rainfall products in tekeze basin, Ethiopia

Accurate and reliable rainfall information is crucial for regional water resource management, particularly in developing countries like Ethiopia. Unfortunately, Ethiopia faces challenges with sparse and inconsistent rainfall measurements, as well as a lack of updated data. As a result, the spatio-temporal characteristics of rainfall are poorly understood. In recent years, satellite-derived rainfall products have emerged as an alternative source of rainfall data to overcome these limitations. This study focuses on validating the performance of three satellite rainfall products, namely PERSIANN_CDR, CHIRPS, and TMPA3B42, over the Tekeze basin in Ethiopia. The evaluation involves assessing the prediction accuracy of these products using various statistical measures and spatial comparisons. The study period extends from 2007 to 2017. The results demonstrate that PERSIANN_CDR exhibits a very low percent bias (PBIAS), while CHIRPS and TMPA3B42 significantly overestimate observed rainfall. Moreover, PERSIANN_CDR performs well in capturing rainfall during the kiremt, belg, and bega seasons, with the lowest root mean square error (RMSE) values of 2.9, 1.4, and 0.8 mm/day, respectively. On the other hand, TMPA3B42 performs poorly during these seasons, showing the largest RMSE values of 3.1, 1.9, and 1.1 mm/day, respectively. In terms of detecting observed rainfall, both PERSIANN_CDR and TMPA3B42 exhibit good skills, while CHIRPS has the lowest detection skill across all seasons. Overall, the findings of this validation study highlight the potential of the PERSIANN_CDR product for various operational applications in the Tekeze basin. It can be utilized for studying rainfall patterns and variability in the East African region.

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.