Trend In Annual Precipitation Research Articles

Contemporary Climate Change in Montenegro

Contemporary Climate Change in Montenegro

Spatio‐temporal analysis of trends in annual maximum rainfall in the North‐West of Algeria: Comparative analysis of recent and old non‐parametric methods

AbstractThis article examines the spatial variability of extreme precipitation trends in northwestern Algeria (Macta) and compares the results obtained from the four recent and old non‐parametric methods. A dataset of annual maximum precipitation consisting of 41 observation years (1970–2010) and 41 rain gauges was used. The results of the four old and new methods used to detect trends, Mann–Kendall (MK), Bravais–Pearson (BP), Spearman (SR) and innovative trend analysis (ITA), show good agreement. They revealed that a decrease in the trend of annual maximum precipitation was detected during the first period (1970–1992) with −44% (MK), −61% (BP), −68% (SR) and −76% (ITA). On the other hand, in the second period (1993–2010), a total shift occurred in which a significant increase in annual maximum precipitation trends was observed with +63% (BP), +34% (MK and SR) and +93% (ITA). These results show the ability of ITA to detect partial trends that the other three tests do not allow. Our results allow decision‐makers to properly design adaptation strategies in the face of the intensification of these extreme events.

Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab

The agriculture sector, due to its significant dependence on climate patterns and water availability, is highly vulnerable to changing climate patterns. Pakistan is an agrarian economy with 30% of its land area under cultivation and 93% of its water resources being utilized for agricultural production. Therefore, the changing climate patterns may adversely affect the agriculture and water resources of the country. This study was conducted to assess the climate variations over the major agro-climatic zones of Sindh and Punjab, which serve as an important hub for the production of major food and cash crops in Pakistan. For this purpose, the climate data of 21 stations were analyzed using the Mann–Kendall test and Sen's slope estimator method for the period 1990–2022. The results obtained from the analysis revealed that, in Sindh, the mean annual temperature rose by ~0.1 to 1.4°C, with ~0.1 to 1.2°C in cotton-wheat Sindh and 0.8 to 1.4°C in rice-other Sindh during the study period. Similarly, in Punjab, the mean annual temperature increased by ~0.1 to 1.0°C, with 0.6 to 0.9°C in cotton-wheat Punjab and 0.2 to 0.6°C in rainfed Punjab. Seasonally, warming was found to be highest during the spring season. The precipitation analysis showed a rising annual precipitation trend in Sindh (+30 to +60 mm) and Punjab (+100 to 300 mm), while the monsoon precipitation increased by ~50 to 200 mm. For winter precipitation, an upward trend was found in mixed Punjab, while the remaining stations showed a declining pattern. Conclusively, the warming temperatures as found in the analysis may result in increased irrigation requirements, soil moisture desiccation, and wilting of crops, ultimately leading to low crop yield and threatening the livelihoods of local farmers. On the other hand, the increasing precipitation may favor national agriculture in terms of less freshwater withdrawals. However, it may also result in increased rainfall-induced floods inundating the crop fields and causing water logging and soil salinization. The study outcomes comprehensively highlighted the prevailing climate trends over the important agro-climatic zones of Pakistan, which may aid in devising an effective climate change adaptation and mitigation strategy to ensure the state of water and food security in the country.

Evaluation and Projection of Climate Change in the Second Songhua River Basin Using CMIP6 Model Simulations

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

Reliability of simulating internal precipitation variability over multi‐timescales using multiple global climate model large ensembles in China

AbstractSingle model initial‐condition large ensembles (SMILEs) are valuable means to study the role of internal climate variability (ICV) in climate change. However, the ability of SMILEs to represent the multi‐timescale ICV is not fully understood, especially in the region of China. The main objective of this work is to assess the ability of six advanced SMILEs with ensemble members ranging from 30 to 100 in simulating ICV of precipitation at various timescales in China, using a set of observational datasets as the benchmark. To have a first insight into the selected models, the mean states and trends of annual precipitation were evaluated at the very beginning. The ICV of precipitation was then calculated at multiple timescales by two widely used methods of representing temporal variability and intermember variability. The minimum required ensemble size for each climate model to robustly capture the true value of ICV is investigated at last. The results show that the multiple SMILEs can capture the basic spatial distribution pattern of ICV across timescales. However, the ICV represented by temporal variability underestimates observed ICV in most regions across timescales. The ICV represented by intermember variability is similar to that represented by temporal variability at the interannual and interdecadal timescales, but significantly larger at the multidecadal timescales. Within ±10% error limit, 20–30 members are sufficient for all climate models at the interannual timescale while ensemble sizes of 74% or more of the full sizes for each climate model are needed at multidecadal timescales.

Analysis of annual and monthly precipitation trends based on long-term data (Case study: Zohreh–Jirahi Basin in Iran)

Analysis of annual and monthly precipitation trends based on long-term data (Case study: Zohreh–Jirahi Basin in Iran)

Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China

In this study, we conducted an analysis of the CN05.1 daily precipitation observation dataset spanning from 1985 to 2014. Subsequently, we ranked the 30 global climate model datasets within the NEX-GDDP-CMIP6 dataset using the RS rank score method. Multi-model weighted-ensemble averaging was then performed based on these RS scores, followed by a revision of the multi-model weighted-ensemble averaging (rs-MME) using the quantile mapping method. The revised rs-MME model data were utilized for simulating precipitation variations within the Yangtze River Basin. We specifically selected 11 extreme-precipitation indices to comprehensively evaluate the capability of the revised rs-MME model data in simulating extreme-precipitation occurrences in the region. Our investigation culminated in predicting the characteristics of precipitation and the potential shifts in extreme-precipitation patterns across the region under three distinct shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for three temporal segments: the Near 21C (2021–2040), Mid 21C (2041–2070), and Late 21C (2071–2100). Our findings reveal that the revised rs-MME model data effectively resolve the issues of the overestimation and underestimation of precipitation data present in the previous model. This leads to an enhanced simulation of mean annual precipitation, the 95th percentile of precipitation, and the extreme-precipitation index for the historical period. However, there are shortcomings in the simulation of linear trends in mean annual precipitation, alongside a significant overestimation of the CWD and CDD indices. Furthermore, our analysis forecasts a noteworthy increase in future mean annual precipitation within the Yangtze River Basin region, with a proportional rise in forced radiation across varying scenarios. Notably, an ascending trend of precipitation is detected at the headwaters of the Yangtze River Basin, specifically under the Late 21C SSP5-8.5 scenario, while a descending trend is observed in other scenarios. Conversely, there is an escalating pattern of precipitation within the middle and lower reaches of the Yangtze River Basin, with most higher-rate changes situated in the middle reaches. Regarding extreme-precipitation indices, similar to the annual average precipitation, a remarkable upsurge is evident in the middle and lower reaches of the Yangtze River Basin, whereas a relatively modest increasing trend prevails at the headwaters of the Yangtze River Basin. Notably, the SSP5-8.5 scenario portrays a substantial increase in extreme-precipitation indices.

Changes of Hydrological Extremes in the Center of Eastern Europe and Their Plausible Causes

Regional studies of precipitation changes over Europe show that its eastern part is characterized by small changes in annual precipitation and insignificant aridity trends compared to central and southern Europe. However, a frequency analysis over the past 30 years showed statistically significant increasing dryness trends in eastern Europe and an increase in the occurrence of extremely high rainfall as well as prolonged no-rain intervals during the warm season. The largest increase in aridity was observed in the western and central parts of Belarus. During 1990–2020, the frequency of dry periods doubled in all river basins along the Black, Caspian, and Baltic Sea water divide areas of eastern Europe. From 1970 to 1990, there were high streamflow rates during the winter low-flow season. Consequently, over the past 50 years, in spring, we observed here a continued decrease in maximal discharges across all river basins. In summer, we detected a statistically significant increase in the number of days with anticyclonic weather over eastern Europe, a decrease in rainfall duration by 15–20%, an increase in daily precipitation maxima by 20–30%, and an increase in the number of days with a low relative humidity by 1–4 days per decade.

Spatio-Temporal Evolution of Rainfall over the Period 1981–2020 and Management of Surface Water Resources in the Nakanbe–Wayen Watershed in Burkina Faso

Spatio-temporal analysis of rainfall trends in a watershed is an effective tool for sustainable water resources management, as it allows for an understanding of the impacts of these changes at the watershed scale. The objective of the present study is to analyze the impacts of climate change on the availability of surface water resources in the Nakanbe–Wayen watershed over the period from 1981 to 2020. The analysis was conducted on in situ rainfall data collected from 14 meteorological stations distributed throughout the watershed and completed with CHIRPS data. Ten precipitation indices, recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices), were calculated using the RClimDex package. The results show changes in the distribution of annual precipitation and an increasing trend in annual precipitation. At the same time, a trend towards an increase in the occurrence and intensity of extreme events was also observed over the last 4 decades. In light of these analyses, it should be emphasized that the increase in precipitation observed in the Nakanbe–Wayen watershed is induced by the increase in the occurrence and intensity of events, as a trend towards an increase in persistent drought periods (CDD) is observed. This indicates that the watershed is suffering from water scarcity. Water stress and water-related hazards have a major impact on communities and ecosystems. In these conditions of vulnerability, the development of risk-management strategies related to water resources is necessary, especially at the local scale. This should be formulated in light of observed and projected climate extremes in order to propose an appropriate and anticipated management strategy for climate risks related to water resources at the watershed scale.

Rainfall Variability and Teleconnections with Large-Scale Atmospheric Circulation Patterns in West-Central Morocco

Morocco is characterized by a semi-arid climate influenced by the Mediterranean, Atlantic, and Saharan environments, resulting in high variability in rainfall and hydrological conditions. Certain regions suffer from insufficient understanding concerning the spatiotemporal patterns of precipitation, along with facing recurrent periods of drought. This study aims to characterize the current trends and periodicities of precipitation in west-central Morocco at monthly and annual scales, using data from six rain gauges. The link between monthly precipitation and both the North Atlantic Oscillation (NAO) and the Western Mediterranean Oscillation (WeMO) indices was tested to identify potential teleconnections with large-scale variability modes. The results reveal interannual variability in precipitation and climate indices, while showing decreasing insignificant trends in annual precipitation. On a monthly scale, temporal precipitation patterns are similar to the annual scale. Furthermore, a remarkably robust and significant component with a periodicity of 6–8 years emerges consistently across all monitoring stations. Intriguingly, this band exhibits a more pronounced presence on the plains as opposed to the mountainous stations. Additionally, it is noteworthy that the NAO modulated winter precipitation, whereas the influence of the WeMO extends until March and April. This mode could be linked to the fluctuations of the WeMO from 1985 to 2005 and, subsequently, to NAO variations. Indeed, this is consistent with the strong significant correlations observed between rainfall and the NAO/WeMO. This study serves as a baseline for future research aiming to understand the influence of climate indices on rainfall in the North African region.

Flow characteristics of the Hornád River and the anthropogenic impact of the Ružín dam for the period 1973–2020

The aim of the study is to analyse the monthly and annual variability of the flow of the Hornad River in the years 1973–2020. The research showed that an upward trend in annual air temperature, precipitation and flows was recorded throughout the catchment area. The analysis of the annual Richard-Baker Index (RBI) and flow variability (Cv) showed a clear downward trend in the variability indexes Cv and Richard-Baker Index in the analyzed multi-year period only at the Kysak station, which is located downstream of the dam. No such changes were observed at the remaining Margecany and Spisska Nova Ves stations, which are located above the dam. The analysis of average monthly Richard-Baker Index values over the course showed greater variation. Between 1973 and 2020, the Richard-Baker Index values ranged from 0 to 0.49 at the Spisska Nova Ves and Margecany hydrological stations, while at the Kysak station, the Richard-Baker Index ranged from 0 to 0.57, indicating large changes in short-term flows. The maximum Richard-Baker Index value occurred in June 1973 (0.57) at the Kysak station. At the hydrological stations above the Ruzin reservoir, the maximum RBI values were recorded in as well as in July 2008 (0.49) at the Margecany station (0.49) and at the Spisska Nova Ves station (0.48), which was the result of very high precipitation (above 170 mm during a month). The values of the Richard-Baker Index at the Kysak station depended on the possibility of water retention in the Ružín I and Ružín II reservoirs and flow control below the dam.

Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes

Abstract The objective of this study is to quantify the spatial dependency and trend of annual maxima precipitation (annual highest daily precipitation, from 1970 to 2020) across selected weather stations in the Nelson Churchill River Basin (NCRB) of North America. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation. The generalized extreme value (GEV) parameters are expressed as simple linear combinations of geographical coordinates (i.e., longitude and latitude) and topography. The results show that topography, geographical coordinates, and time (as a temporal covariate) were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted SPEV was used to predict the 25- and 50-year return period levels. The fitted Extremal-t max-stable process model captured the spatial dependency structure of the extreme precipitation in the NCRB. The study is relevant in quantifying the spatial dependency structure of extreme precipitation in the Northern Great Plains. The result will contribute as a decision-support system in climate adaptation strategies in the United States and Canada.

How climate change could affect different cities in Canada and what that means for the risks to the built-environment functions

This research aims to advance risk perceptions of climate change and understanding of the potential needs of municipal-scale climate adaptation across seventeen Canadian cities. To do so, the rate and direction of changes in a wide variety of climate variables are examined in all the capital cities and some other major cities like Montreal, Vancouver, and Calgary, by analyzing downscaled data from twenty-six General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) to the year 2100, using the modified Mann-Kendall test and Euclidean distance. The findings of this study suggest that under SSP5–8.5 scenario, Canadian cities will experience more extreme heat and heavy rainfall but less extreme cold in the future, especially when compared to SSP2–4.5, where for example, the number of models showing an increasing trend for annual precipitation is about 60% higher under SSP5–8.5 than under SSP2–4.5 over the 17 cities. In addition, using Epps-Singleton test, climate change in neighboring cities is compared to see how similar adaptation plans are possible. The results show that coastal cities, even those located relatively close to one another, such as Vancouver and Victoria, follow different trends for most of the variables (22 out of 28). Furthermore, a qualitative assessment of which built environment aspects of these cities may require more rapid modification than others is provided to assist decision-makers and planners in resilience planning and resource allocation.

Observed Changes in Interannual Precipitation Variability in the United States

AbstractCharacterizing changes in precipitation patterns over time is critical for hydrologically dependent fields like water resource management and agriculture. Here, we explore observed trends in interannual precipitation variability using a suite of metrics that describe changes in precipitation over time. We analyze daily in situ Global Historical Climatology Network precipitation data from 1970 to present over 17 internally consistent sub‐national United States domains using a regional Mann‐Kendall trend test. We find robustly increasing trends in annual mean precipitation and wet day frequency for most of the central and eastern U.S., but decreasing trends in the western U.S. Importantly, we identify widespread significant trends in interannual precipitation variability, with increasing variability in the southeast, decreasing variability in the far west, and mixed signals in the Rocky Mountains and north‐central U.S. Our results provide important context for water resource managers and a new observational standard for climate model performance assessments.

Assessing the skill of gridded satellite and reanalysis precipitation products over in East and Southern Africa

Validation of gridded precipitation products (GPP) increases the users’ confidence and highlights possible improvements in the algorithms to handle complex rain-forming processes. We evaluated the skill of three GGPs (CHIRPS-v2, CHELSA, and TerraClimate) in estimating the rain gauge observations and compared the precipitation trends derived from these products across the East and Southern Africa (ESA) region. We used Taylor diagrams and Kling-Gupta Efficiency (KGE) to assess the accuracy. A modified Mann-Kendal test and a Sen’s slope estimator were utilized to determine the trends’ significance and magnitude, respectively. The three GPPs had varied performance over temporal and altitudinal ranges. The skill of the three GPPs, at a monthly scale, was generally high but showed lower performance at elevations over 1500 masl, especially during the October-November-December (OND) season. The three GPPs performed equally well between the 1001 – 1500 masl elevation range. CHELSA-v2.1 was most accurate at 0-500 masl but had the lowest skill in both 501 – 1000 and above 1500 masl elevations, which caused over-estimation of the annual and seasonal precipitation trends over mountainous terrain and large inland water bodies. The quantified precipitation trends revealed high spatial-temporal variability. Generally, the skill and precipitation trends derived from CHIRPS-v2 and TC data showed substantial convergence except in Tanzania. Our results emphasize the importance of validating climate datasets to avoid error propagation in different models and applications. Moreover, we demonstrate that new or higher-resolution precipitation data are not always accurate since an algorithm update can introduce artifacts or biases.

Effects of Wind-Induced Error on the Climatology and Trends of Observed Precipitation in China from 1960 to 2018

Abstract Since the 1950s, precipitation has been measured at national weather stations in China using national standard precipitation gauges. Gauges without a wind fence can significantly underestimate precipitation amounts, while this undercatch bias is closely related to surface wind speed and precipitation type. The observed surface wind speed across China has substantially declined during the past decades. Therefore, this study investigated the wind-induced error of the observed precipitation and its impact on regional and national mean trends in precipitation over China due to the reduction in surface wind speed. It was found that the wind-induced error for the mean annual precipitation nationwide was 29.28 mm yr−1, accounting for 3.92% of total precipitation amount. The variation of precipitation at the regional scale was large but the trends were both positive and negative, approximately cancelling at the national level and resulting in a small national mean trend. The raw observation data showed that the national mean precipitation increased at a rate of 1.85 mm yr−1 (10 a)−1 from 1960 to 2018, which was reduced to 0.33 mm yr−1 (10 a)−1 after correction, demonstrating that the correction of wind-induced error had an important impact on the trend of annual precipitation. Meanwhile, the reduction of surface wind speed was consistent at both the regional and national levels. On average, the wind-induced errors decreased at rates of −1.52, −1.34, and −0.14 mm yr−1 (10 a)−1 for total precipitation, rainfall, and snowfall, respectively. It illustrates that the decreases of the wind-induced error result in the increasing precipitation of raw observation.

Statistical analysis of climate change over Hanumangarh district

Abstract Precipitation is the most critical element of the hydrological cycle that might influence the frequency of floods or drought. Therefore, for drought and flood forecasting, knowing the precipitation pattern and its trend is mandatory. The research aims to estimate the monthly, seasonal, and annual precipitation trend using statistical (Mann Kendall test & Sen's Slope estimator) and graphical (Innovative Trend Analysis method) for the Hanumangarh district of N-E Rajasthan. The mean monthly precipitation data were gathered from the India-WRIS from 1901 to 2022 (122 years). Seasonal and annual precipitation trend variations were examined by statistical methods and compared with the graphical method. The study results conclude that precipitation trends showed variability for the SW monsoon season from the graphical to the statistical method. For the SW monsoon season, the decreasing trend was seen using statistical methods. In contrast, the graphical method revealed an increasing trend. During the annual precipitation, increasing trends were seen using statistical and graphical methods. It is found that the ITA method is more efficient in finding trends because it does not assume any assumptions like that of the MK test. This study will help water resources engineers and local people with sustainable management, planning, and development of water resources in the Hanumangarh district.

Assessment of the potential impacts of climate changes on Syr Darya watershed: A hybrid ensemble analysis method

Study regionSyr Darya watershed, Central Asia. Study focusClimate change has the potential to significantly impact the precipitation patterns, available water resources, food security, and ecosystem balance of a watershed. However, limited understanding exists of the temperature, precipitation, and streamflow trends in the Syr Darya watershed under the influence of climate change due to the uncertainties associated with climate change and the incompleteness of observational data. In this study, a multi-GCMs based statistical ensemble analysis (MGSEA) method is developed to effectively reflect the uncertainty of climate predictions and comprehensively assess the impact of climate change on the Syr Darya watershed during the period from 2021 to 2100. New hydrological insights for the region: The variations in temperature, precipitation, and streamflow were assessed by analyzing the projection results of various scenario combinations. Compared to the baseline (1960–2005), the findings reveal a rise in the annual average temperature ranging from 0.2 to 3.8 ℃ for RCP 4.5 and from 1.4 to 5.5 ℃ for RCP 8.5 in the 2080s. Additionally, the research confirms a downward trend in annual precipitation, with decreases of 3.7–27.8% for RCP 4.5 and 5.1–47.7% for RCP 8.5. The results of streamflow analysis exhibit an increasing trend during winter and autumn and a decreasing trend during summer and spring. The research outcomes obtained from MGSEA can be utilized for supporting water resources planning and management.

Spatiotemporal Precipitation Trends and Associated Large-Scale Teleconnections in Northern Pakistan

The effects of climate change are unparalleled in magnitude, ranging from changing weather patterns that endanger food production to increasing sea levels that increase the likelihood of catastrophic flooding. Therefore, determining the extent of such variations on regional and local scales is imperative. We used monthly precipitation data from 25 meteorological stations in northern Pakistan (NP) to document the observed changes in seasonal and annual precipitation. The station density in the NP is small and unevenly distributed; therefore, ERA-5 reanalysis data were used to supplement the observed dataset to assess the spatial trends in NP. The non-parametric Mann–Kendall (MK), Sen’s Slope estimator (SSE), and Sequential Mann–Kendall (SQMK) tests were performed to assess the trends. In addition, the wavelet analysis technique was used to determine the association of precipitation with various oceanic indices from 1960 to 2016. Results indicate that maximum precipitation was shown in the annual and summer seasons. In NP, annual, winter, spring, and summer precipitation declined, while an increase in autumn was observed at a rate of 0.43 mm/decade between 1989 and 2016. The spatial trends for observed and ERA-5 reanalysis datasets were almost similar in winter, spring, and autumn; however, some disagreement was observed in both datasets in the summer and annual precipitation trends in NP during 1960–2016. Between 1989 and 2016, summer and annual precipitation increased significantly in Region III. However, seasonal and annual precipitation decreased in NP between 1960 and 2016. Moreover, there were no prominent trends in annual precipitation until the mid-1980s, but an apparent increase from 1985 onwards. Annual precipitation increased in all elevations except at the 500–1000 m zone. The ENSO (El Niño–Southern Oscillation) shared notable interannual coherences among all indices above 16–64 months. Inter-decadal coherence with the ENSO, AO (Arctic Oscillation), and PDO (Pacific Decadal Oscillation) in NP for 128 months and above. Generally, AO, AMO (Atlantic Multidecadal Oscillation), and NAO (North Atlantic Oscillation) exhibited less coherence with precipitation in NP. The regression of seasonal and annual precipitation revealed that winter and spring precipitation levels had higher linear regression with the AO and ENSO, respectively, while both the AO and ENSO also dominated at the annual scale. Similarly, the IOD and PDO indices had a higher influence in summer precipitation. The findings may help water resource managers and climate researchers develop a contingency plan for better water resource management policies in the face of changing climate change in Pakistan, particularly in NP.

Analysis of Hydrometeorological Trends and Drought Severity in Water-Demanding Mediterranean Islands under Climate Change Conditions

Global climate change has emerged as a problem in recent years, and its effects will likely continue to increase in the future. Several scientific studies conducted in the Mediterranean region have demonstrated relatively stationary trends for annual precipitation and significant upward trends for mean annual temperature. These trends present several implications, especially in the Greek islands that serve as major summer tourist destinations where the population is already unable to meet their water demands. The aim of this study is to investigate both long- and short-term variations in temperature and precipitation on three Greek islands in the Mediterranean Sea (Mykonos, Naxos, and Kos). The temperature and rainfall trends, as well as their magnitudes at yearly, seasonal, and monthly time steps, were determined using the non-parametric Mann–Kendall trend test. The Standardized Precipitation Index (SPI) was employed to identify the drought periods. According to the results, precipitation slightly increased (almost stationary) in the three islands, although this rise was not statistically significant. All three islands experienced a sharp and statistically significant increase in their mean annual air temperatures. The region may experience drought episodes as a result of the high temperature increase, which would drastically reduce the amount of water, available for use due to the increased evapotranspiration. For the Mediterranean region, the necessity for a drought management strategy to stop or diminish the severity of drought episodes and their effects has grown into a matter of great concern. It is crucial to take measures and conduct relevant research in order to create the conditions for adaptation and mitigation of climate change consequences and the increased appearance of drought phenomena.