Precipitation Index Research Articles

Using an integrated hydro-economic model to determine the drought and energy relationship in the Upper Euphrates Basin

ABSTRACT Decreasing precipitation in the Upper Euphrates Basin and the negative impact of climate change directly affect water resources and hydroelectricity generation in the basin. This basin, which contains the largest dams in terms of hydroelectricity generation potential, requires research studies to assess and characterize drought for risk prevention and mitigation applicable to water resources management. To better assess drought in the upper Euphrates Basin due to recent warming, FEHEM is developed, a hydro-economic optimization model of the integrated reservoir system of the Upper Euphrates Basin. Using a historical hydrological dataset, water management and hydroelectric operations are evaluated with a linear programming model at monthly time steps. This paper uses two different drought indices: (1) the standardized precipitation index, which is based on precipitation alone; and (2) the reconnaissance drought index, which takes into account both evaporation and precipitation. These indices were used to evaluate the impact of temporal drought characteristics in the Upper Euphrates Basin on the hydropower generation of 10 dams with a total installed capacity of over 3255 MW in the basin, based on 45 years of precipitation data from more than a hundred measuring stations in the basin.

Оценка частоты возникновения засухи в Файзабадском районе (Таджикистан) в зависимости от метеорологических условий

The results of the analysis of the manifestation of drought in the Faizabad region based on calculations of the standardized precipitation and evapotranspiration index (SPEI) are presented. It has been established that there is no definite pattern in the occurrence of drought, even for a long period. By comparing the number and duration of droughts in the Fayzabad district with the climatic characteristics of the Fayzabad district, a close dependence of drought on the average annual temperature and precipitation has been established.

Extreme climate events and future population exposure under climate change in the Huaihe River basin of China based on CMIP6 multimodel ensembles projections

AbstractThe Huaihe River basin (HRB) of China located in the climate transition zone between warm temperate and subtropical areas is highly sensitive to climatic change. However, the changes in future climate extreme events under anthropogenic warming and the population exposure to these climate extremes in HRB remain unexplored. Here, using the eight commonly used extreme climate indices and based on the bias‐corrections of 16 global climate models (GCMs) in CMIP6, we present a projection and uncertainty analysis of extreme events and investigate the corresponding population exposure risk in HRB under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, SSP5‐8.5). The 16‐GCM ensemble mean projects an evident warming trend under all three scenarios with a total increase of 25.6–68.0 days in summer days (>25°C) by the end of the century in HRB. Larger increases (decreases) in maximum and minimum temperatures (frost days) are projected in the western HRB. Very heavy rain days (R20mm), maximum 5‐day precipitation (RX5day) and simple daily intensity index (SDII) will experience intensification across most of HRB (especially in southern and western HRB). The consecutive dry days is projected to decrease in northwestern HRB and increase in southern HRB. However, there is a large spatial variability in GCM uncertainty with a higher SSP scenario generally having higher uncertainty. Increases in summer days and R20mm exacerbate population exposure in HRB in near future (2030–2059), but in far future (2070–2099) although summer days (R20mm) continues to rise, population exposure is expected to decrease due to the rapid decline in population density.

Evaluation of the Usability of the Rainfall Anomaly Index (RAI) Instead of the Standard Precipitation Index (SPI)

Evaluation of the Usability of the Rainfall Anomaly Index (RAI) Instead of the Standard Precipitation Index (SPI)

Evaluation of the impact of anthropogenic storage on the hydrological drought propagation in two contrasting semi-arid river basins

ABSTRACT This study quantitatively investigated reservoir-effects on drought propagation in two semi-arid river basins: India’s Tapi basin with the Ukai reservoir and Uzbekistan’s Chirchik basin with the Charvak reservoir. Meteorological drought (MD) is analyzed using the Standardized Precipitation Index (SPI) and hydrological drought (HD) using the Standardized Streamflow Index (SSI) for the duration from 1980 to 2004. Both the river basins, especially in upstream reservoir areas, exhibited a notable correlation between HD and MD. Reservoir operation was observed to reduce the downstream MD–HD correlation at shorter SPI timescales. Hit-score-based evaluations indicated that reservoir operation has induced changes in the drought propagation patterns for both river basins. Due to the contrasting characteristics, the river basins showed a significant variation in the drought propagation time (DPT), with distinct influences from monsoon (Tapi) and snow-melting (Chirchik). The average DPT (average DPT over 12 months) for the reservoir-influenced part of the Tapi (∼ six months) and Chirchik (∼ nine months) basins is higher than that of the natural parts of both basins (Tapi: ∼ four months; Chirchik: ∼ six months) as a result of the natural and anthropogenic storage influence.

Interactive Changes in Climatic and Hydrological Droughts, Water Quality, and Land Use/Cover of Tajan Watershed, Northern Iran

In response to novel and complex uncertainties, the present research is conducted to characterize the most significant indicators of watershed health including drought, water quality, and vegetation for the Tajan watershed, Mazandaran, Iran. The Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are, respectively, used to quantify the meteorological and hydrological droughts in the present (1993–2020) and future (2023–2050) employing optimistic RCP2.6 and pessimistic RCP8.5 scenarios. To concoct discharge data for the future, IHACRES v1.0 software is used with a Nash–Sutcliffe coefficient (NSE) of 0.48 and a coefficient of determination (R2) of 0.58. Maps of land use and Normalized Difference Vegetation Index (NDVI) are also prepared using Landsat images. Subsequently, the surface water quality is assessed using AqQA v1.1.0 software. The results show the difference in the severity of future meteorological droughts in different stations. In addition, the predominance of non-drought (SDI ≥ 0) or mild drought (−1 ≤ SDI < 0) is indicated for future hydrology. The land use changes show a decrease in rangeland (−5.47%) and an increase in residential land (9.17%). The water quality analysis also indicates an increase in carbonate ions in the watershed outlet. Communicating the relationships between study indicators, which is a big gap in the current watershed management approach, avoids future failures and catastrophes.

Streamflow response to drought in a managed coast redwood catchment

AbstractA 60‐year precipitation and streamflow record from the Caspar Creek Experimental Watersheds in northern California was used to explore the propagation of meteorological drought to hydrological drought. Standardized precipitation and runoff indices were calculated for the two forested catchments using integration periods of 12, 24, and 36 months. The resulting time series were used to define three severe drought events (1976–1977, 2013–2014, and 2020–2022). The earliest drought followed the 1971–1973 harvest of the 417 ha South Fork (SF) watershed, a second followed the 1989–1992 harvest of the 479 ha North Fork watershed, and a third followed the 2017–2019 reentry harvest of the SF. From these time series, we calculated drought metrics and anomalies to model differences in catchment responses in the context of climate and management. The meteorological drought in the 1977 event was more severe and extreme than the streamflow response. Both of the 21st Century droughts were hydrologically more severe than the 1977 drought. Timber harvest initially shortened and reduced streamflow drought (1977 and 2021) but prolonged and intensified the 2014 streamflow drought. Declining fall precipitation has reduced streamflows, thereby impeding salmonid migration and exacerbating impacts on native fish. Our results provide new insights into the role of climate variation, particularly long‐term and seasonal drought dynamics, in managed forests along the North American Pacific coast.

Seasonal and Circadian Variation of Non-Volatile Metabolites in Lippia alba Leaves.

The present study describes the seasonal and circadian variations of the major compounds from Lippia alba leaves. SPSS was used to identify, quantify, and associate the variations in the secondary metabolites of this species through HPLC/DAD analysis of the leaves hydroethanolic extracts of six selected L. alba specimens. For the circadian study, the samples were collected at four different daily hours in each year's season. For the seasonal study, the samples were collected monthly from the same individuals for two consecutive years (2018 and 2019). These samples were analyzed and quantified using a validated HPLC method for flavonoids, iridoids, and phenyl ethanoid glycoside. Mussaenoside, acteoside, and tricin-7-O-diglucuronide showed a moderate positive correlation between their biosynthesis and the precipitation index, while epi-loganin had a moderate negative correlation. Acteoside showed a moderate positive correlation between the minimum registered temperature and its production. Compared with previous studies, a drastic reduction (about 95 %) in the production of tricin-7-O-diglucuronide compared with previous study and this difference could be attributed to the plant's aging. Thus, the data demonstrated that lower temperatures and high rainfall could favor the production of the major L. alba active compounds (acteoside and tricin-7-O-diglucuronide) and that older plants harm their production.

Environmental drivers behind the exceptional increase in cyanobacterial blooms in Okavango Delta, Botswana

The Okavango Delta region in Botswana experienced exceptionally intense landscape-wide cyanobacterial harmful algal blooms (CyanoHABs) in 2020. In this study, the drivers behind CyanoHABs were determined from thirteen independent environmental variables, including vegetation indices, climate and meteorological parameters, and landscape variables. Annual Land Use Land Cover (LULC) maps were created from 2017 to 2020, with ∼89% accuracy to compute landscape variables such as LULC change. Generalized Additive Models (GAM) and Structural Equation Models (SEM) were used to determine the most important drivers behind the CyanoHABs. Normalized Difference Chlorophyll Index (NDCI) and Green Line Height (GLH) algorithms served as proxies for chlorophyll-a (green algae) and phycocyanin (cyanobacteria) concentrations. GAM models showed that seven out of the thirteen variables explained 89.9% of the variance for GLH. The models showcased that climate variables, including monthly precipitation (8.8%) and Palmer Severity Drought Index- PDSI (3.2%), along with landscape variables such as changes in Wetlands area (7.5%), and Normalized Difference Vegetation Index (NDVI) (5.4%) were the determining drivers behind the increased cyanobacterial activity within the Delta. Both PDSI and NDVI showed negative correlations with GLH, indicating that increased drought conditions could have led to large increases in toxic CyanoHAB activity within the region. This study provides new information about environmental drivers which can help monitor and predict regions at risk of future severe CyanoHABs outbreaks in the Okavango Delta, Botswana, and other similar data-scarce and ecologically sensitive areas in Africa.Plain Language Summary: The waters of the Okavango Delta in Northern Botswana experienced an exceptional increase in toxic cyanobacterial activity in recent years. Cyanobacterial blooms have been shown to affect local communities and wildlife in the past. To determine the drivers behind this increased bloom activity, we analyzed the effects of thirteen independent environmental variables using two different statistical models. Within this research, we focused on vegetation indices, meteorological, and landscape variables, as previous studies have shown their effect on cyanobacterial activity in other parts of the world. While driver determination for cyanobacteria has been done before, the environmental conditions most important for cyanobacterial growth can be specific to the geographic setting of a study site. The statistical analysis indicated that the increases in cyanobacterial bloom activity within the region were mainly driven by persistent drier conditions. To our knowledge, this is the first study to determine the driving factors behind cyanobacterial activity in this region of the world. Our findings will help to predict and monitor areas at risk of future severe cyanobacterial blooms in the Okavango Delta and other similar African ecosystems.

Skillful seasonal prediction of Afro-Asian summer monsoon precipitation with a merged machine learning and large ensemble approach

Afro-Asian summer monsoon precipitation (AfroASMP) is the life blood of billions of people living in many developing countries covering West Africa and Asia. Its complex variabilities are always accompanied by natural disasters like floods, landslides and droughts. Reliable AfroASMP prediction several months in advance is valuable for not only decision-makers but also regional socioeconomic sustainability. To address the current predicament of the AfroASMP seasonal prediction, this study provides an effective machine-learning model (Y-model). Y-model uses the monsoon related big climate data for searching the potential predictors, encompassing atmospheric internal factors and external forcings. Only the predictors associated with significant anomalies in summer horizonal winds at 850 hPa over the monsoon domain are retained. These selected predictors are then reorganized into a large ensemble based upon different thresholds of four fundamental principles. These principles include the focused sample sizes, the relationships between predictors and predictand, the independence among predictors, and the extremities of predictors in the forecast year. Real-time predictions can be generated based on the ensemble mean of skillful members during an independent hindcast period. Y-model skillfully predicts four monsoon precipitation indices of AfroASMP during 2011–2022 at lead 4–12 months, correlation skills range from 0.58 to 0.90 and root mean square error skills are reduced by 11–53% compared to CFS v2 model at lead 1 month. This study offers an effective method for preprocessing predictors in seasonal climate prediction.

Decoding uranium variation over the Indian peninsula through leveraging standardized precipitation evapotranspiration indices and groundwater level fluctuations

The present study aims to predict the concentration of uranium (U) in groundwater in India by utilizing water quantity indicators, such as Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), and Gravity Recovery and Climate Experiment (GRACE) based groundwater levels. The study adopts a multi-scale approach, ranging from state-level to agroclimatic zones. The findings indicate that the highest levels of U (101–500 μg.L−1), which surpass the World Health Organization (WHO) prescribed limit of 30 μg.L−1 for drinking water, are primarily concentrated in India's plateau and hills regions. The GRACE data map portrays a downward trend in groundwater levels throughout India, with the mid-Gangetic plains experiencing the most significant decline. The meteorological aspect of the study, as indicated by SPI and SPEI, reveals that the plateau and hills region is experiencing a decline in rainfall. The SPEI further underscores the grim picture of decreasing precipitation in northern India. Additionally, the study employs cluster analysis to cluster states according to the division of agro-climatic zones. Lastly, the study employs a random forest algorithm to assess the relative importance of each predictor and predict U concentration under the trinity of precipitation, extraction, and evaporation. The most significant contribution of this work is to identify the hotspots in India that require the most attention in terms of U toxicity owing to groundwater decline. Overall, this study highlights the need for immediate attention to mitigate the adverse impacts of U contamination and aims at sensitizing the stakeholders towards the compelling need to fulfil SDG-3 (health aspects due to U hazard) and SDG-6 (groundwater over-exploitation and deteriorating water quality).

Impacts of hydrometeorological regime shifts on drought Propagation: The meteorological to hydrological perspective

Exploring drought propagation relationships and thresholds is crucial for drought control and mitigation. However, previous studies have overlooked the impacts of regime shifts in trends of hydrometeorological variables over time on drought propagation and its thresholds. This study proposes an integrated framework focusing on drought propagation from meteorological to hydrological drought. The regime shift periods, both increasing and decreasing trends, were identified using the cumulative difference curve-rank test on precipitation and streamflow at an annual time scale. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used as meteorological and hydrological drought indices, respectively. Additionally, the propagation relationships and thresholds from meteorological drought to hydrological drought were identified and compared across various regime shift periods. The integrated framework’s applicability was verified by applying it to the Three-Rivers Headwaters region of China, which encompasses the Yellow River, Yangtze River, and Lancang River. This region is strategically important for China and has relatively little human activity. The framework revealed the variability in drought propagation and thresholds during different regime shift periods. The response sensitivity of SSI to SPI and the probabilities and thresholds for propagation from meteorological to hydrological drought differed between periods of increasing and decreasing trends in hydrometeorological variables. When regime shift characteristics were considered, the thresholds for meteorological drought propagating to hydrological drought were 2.81 %–16.90 % higher. These results indicated that, when examining drought propagation relationships and thresholds, accounting for regime shifts in hydrometeorological variables can improve early drought warning and enhance drought control and mitigation efforts.

Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition

Study region: Six regions in Algeria have been selected as follows: Ain Elhadjel, Msaad, Boussaada, Elkantara, M’sila and M’doukel.Study focus: This study focused on creating a novel hybrid VMD-ELM approach, established by combining the Variational Mode Decomposition (VMD) technique and the Extreme Learning Machine (ELM) algorithm as a preprocessing technique for predicting future droughts. The first 6 and 12-month SPI values 1, 2, and 3-month lead time values were estimated with the ELM algorithm. After that, meteorological variables and Standard Precipitation Index (SPI) values, divided into subcomponents with VMD, are presented to the ELM model, and a drought forecasting model is developed. Model performances were evaluated according to various visual and statistical criteria.New hydrological insights for the region: Soft computing techniques have become the preferred method for producing predictions due to their ability to minimize development time, require minimal input, and offer a relatively less complex approach when compared to dynamic or physical models. As a result of the analysis, it has been determined that the highest prediction accuracies are generally obtained in VMD-ELM models and SPI predictions with a 1-month lead time. The study outputs give important ideas to mite donors regarding water resource planning and climate change adaptation strategies in the study area and can be applied to other arid and semi-arid environments.

El Niño’s Implications for the Victoria Falls Resort and Tourism Economy in the Era of Climate Change

Scientists agree that as global temperatures rise, the intensity of the El Niño–Southern Oscillation (ENSO) phenomenon also increases. In southern Africa, El Niño events often cause severe droughts that adversely affect a region’s gross domestic product (GDP). However, the impact of El Niño on the tourism industry in this area has not been studied sufficiently. This study examines how frequently El Niño events occur and their consequences on the Victoria Falls resort. It specifically focuses on the potential implications for the regional tourism economy during the 2023–2024 El Niño event. This study critically analyses potential strategies for tourism businesses to enhance their resilience in light of the increasing intensity of El Niño events in the region. Using the Niño 3.4 index, it has been determined that there have been at least four extreme El Niño events since 1980: 1983, 1998, and 2016, and most recently, in 2023/24. Analysis of the Standardized Precipitation Index (SPI) and hydrological data from the Victoria Falls hydrological station showed that El Niño events significantly affect water flows at Victoria Falls, negatively impacting the resort’s reputation and brand. Examination of tourism statistics reveals that drought often leads to a decline in tourism, negatively affecting tourist resort towns’ economies. Given the projected record-low water levels for late 2024, this study recommends implementing improved communication and marketing strategies that highlight low-water activities and other non-water-dependent tourism offerings at the resort to ensure the resilience of the tourism economy. There is an urgent need to promote resorts as year-round destinations to support the climate-resilient tourism sector.

The difference in the uncertainty sources between future projections of mean and extreme precipitation over East Asia

As the incidence of extreme precipitation events attributable to global climate change increases, providing policymakers with accurate model predictions is of the utmost importance. However, model projections have inherent uncertainties. The present study attempted to distinguish the sources of the uncertainty of the mean and extreme precipitation projections in the East Asia region using the mean boreal summer precipitation, simple precipitation intensity index (SDII), maximum cumulative 5 day precipitation, and annual maximum daily precipitation (Rx1d). The results show that while the mean precipitation was projected to change very little regardless of the scenario, more extreme indices were projected to increase considerably by the end of the century, particularly in the high-emissions scenarios. On average, model uncertainty accounted for the largest part of the uncertainty. However, for Rx1d in the 2030s, as well as mean and SDII in some regions until the 2060s, the internal variability was the largest contributor. In addition, whilst scenario uncertainty accounted for a negligible proportion of average precipitation variability, for the more extreme the precipitation indices, scenario uncertainty contribution to total variability by the end of the century was significant; namely, the scenario uncertainty contribution was overall highest for the maximum one-day precipitation. Additionally, comparatively wetter regions had greater overall projection uncertainties, especially uncertainty arising from internal variability, likely due to the influence of interannual variability from the EA summer monsoon.

The Long-Term Effects of Barren Land Afforestation on Plant Productivity, Soil Fertility, and Soil Moisture in China: A Meta-Analysis.

As global ecological degradation intensifies, the long-term impacts of afforestation on productivity and soil fertility in barren lands have become critical in improving global ecological security and productivity. Through meta-analysis, this study integrates data from 109 barren land afforestation sites across China, aiming to comprehensively analyze the effects on plant productivity and soil fertility while identifying the key environmental drivers of these changes. We found that afforestation consistently enhances plant productivity across 60 years. However, soil fertility and moisture initially surged significantly after afforestation but gradually declined after the first decade, indicating the limited long-term benefits. Climatic factors, namely precipitation and humidity index, are crucial in enhancing plant productivity, while geographic factors, specifically lower elevations and gentler slopes, are associated with greater increases in soil fertility. Elevation and slope are two key factors that influence soil moisture after afforestation. These findings highlight the need for ongoing soil management and ecological maintenance in afforestation projects to sustain the soil fertility benefits. Our study provides a robust scientific foundation for afforestation strategies aimed at barren land restoration and offers valuable insights for policy formulation in barren land afforestation.

Predicting Drought in East Nusa Tenggara: A Novel Approach Using Wavelet Fuzzy Logic and Support Vector Machines

The water crisis, or what is hereinafter referred to as drought, has become a systemic and crucial problem in several regions in Indonesia. Indonesia is an agricultural country, where the presence of water is very influential so that drought can become a natural disaster if it starts to cause an area to lose its source of income due to disturbances in agriculture and the ecosystem it causes. Drought forecasting can provide support solutions in preventing the impact of drought. In this paper, we compare the performance of wavelet fuzzy logic and the support vector machine (SVM) as a supervised learning method for drought forecasting in East Nusa Tenggara. This study examines the monthly rainfall data for 1999-2015 which is the basis for calculating the drought index based on the Standardized Precipitation Index (SPI). The SPI value used is SPI-3 at a station in East Nusa Tenggara. The performance of models is compareded on R2. The results showed that R2 of wavelet fuzzy logic is smaller than one of SVMVM is better than the wavelet fuzzy logic for forecasting SPI value of drought in East Nusa Tenggara.

Quantifying the Impacts of Dry–Wet Combination Events on Vegetation Vulnerability in the Loess Plateau under a Changing Environment

Extreme drought and flood events, as well as their combined events, pose significant challenges to global sustainable socio-economic development and ecological health. However, the impact of dry–wet combination events (DWCEs) on vegetation vulnerability remains to be investigated. The Loess Plateau (LP) was selected as the study area to explore the response time of vegetation to precipitation index changes by optimal correlation coefficient; then, the impact of different DWCEs on vegetation vulnerability under moderate and severe scenarios was analyzed; finally, a vegetation loss probability model was constructed based on the copula function and Bayesian framework, to quantify the vegetation loss probability under DWCEs stress. The results indicate that: (1) normalized difference vegetation index (NDVI) shows an upward trend in spring, summer, and autumn, with the proportion of areas are 90.5%, 86.2%, and 95.4%, respectively, and show an insignificant trend in winter; (2) the response time of vegetation to precipitation index changes tends to be one or two seasons; (3) moderate scenarios have more influence than severe scenarios, dry-to-wet events (DWEs), wet-to-dry events (WDE) and continuous dry events (CDE) in spring-summer have a significant impact on summer vegetation of Ningxia and Shanxi, and WDE and CDE have a higher impact on autumn vegetation. (4) in terms of the probability of vegetation loss, DWE, and CDE cause higher losses to summer vegetation, while WDE and CDE cause higher losses to autumn vegetation. This study quantifies the impact of adjacent seasonal DWCE stress on future vegetation vulnerability.

Exploring short- and long-term meteorological drought parameters in the Vaippar Basin of Southern India

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates.

Analysis of Spatial and Temporal Distribution and Changes in Extreme Climate Events in Northwest China from 1960 to 2021: A Case Study of Xinjiang

Xinjiang, as a climate-sensitive region in Northwest China, holds significant importance in studying extreme climate events for agricultural production and socioeconomic development. Using data spanning from 1960 to 2021 from 52 meteorological stations across Xinjiang, encompassing 23 indices of extreme climate events, the 5-year moving average, linear trend fitting, and inverse distance weighting (IDW) are used to analyze the distribution patterns and temporal changes in extreme climatic phenomena within the region. The results indicate that, over the period from 1960 to 2021, the Amplitude Temperature Index, Heat Index, and Warm Spell Duration Index in Xinjiang exhibited a marked increasing trend, whereas the Cold Index and Cold Spell Duration Index displayed a significant decreasing trend. The range of changes in the extreme temperature indices from 1990 to 2021 is higher than that of 1960 to 1989. The areas with high values of amplitude temperature extreme indices are primarily concentrated in the southern part, while the areas with high values of cold indices are mainly distributed in the northern part. The upward/downward trends all account for over 80.00% of the entire region. The precipitation scale indices, precipitation day indices, intense precipitation index, and extreme precipitation index all showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indices from 1990 to 2021 was lower than that from 1960 to 1989. Furthermore, areas with high precipitation values and regions with high trend values of climate tendency are predominantly concentrated in the northern and western parts of Xinjiang, with over 71.00% of the entire region experiencing an upward trend. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.