Climate-Driven Changes in the Projected Annual and Seasonal Precipitation over the Northern Highlands of Pakistan

Abstract

Precipitation plays a critical role in the hydrological cycle and significantly influences the biodiversity of the Earth’s ecosystems. It also regulates socioeconomic systems by impacting agricultural production and water resources. Analyzing climate-driven changes in precipitation patterns is essential for understanding the hydrological cycle’s response to global warming. This study analyzed the projections of five general circulation models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to evaluate variations in the seasonal and annual patterns of future precipitation over the northern highlands of Pakistan (NHP). The analysis focused on precipitation variations projected for the near future (2021–2050), in comparison to the historical climate (1985–2014), utilizing two combined scenarios from the Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP2-4.5 and SSP5-8.5). This study employed the multi-model ensemble (MME) approach, which demonstrated notable seasonal and annual variations in precipitation across the NHP. The average annual precipitation is expected to decrease in both scenarios, with SSP2-4.5 expecting a reduction of −21.42% and SSP5-8.5 expecting a decrease of −22.43%, compared to the historical average precipitation. In both scenarios, the seasonal precipitation patterns are similar. However, the changes are more noticeable in the spring and summer. Both SSPs predict a 15% decrease in summer precipitation, while SSP2-4.5 and SSP5-8.5 predict a 5% and 4% decrease in spring precipitation, respectively. These changes can result in more frequent and intense periods of drought, which might adversely impact agriculture, human health, the environment, hydropower generation, and the surrounding ecosystem. This study provides important insights into projected seasonal and annual precipitation changes over the NHP, which is particularly susceptible to the effects of climate change. Thus, it is crucial to understand these predicted changes in precipitation in order to develop strategies for adapting to the climate, assuring water security, and promoting sustainable agricultural practices in this area.

1. Introduction

Precipitation plays a crucial role in the hydrologic cycle and significantly impacts the biodiversity of terrestrial ecosystems. It influences socioeconomic systems through its impact on water resources and agricultural productivity. Shifts in precipitation patterns, amounts, and intensities are occurring globally due to climate change. Numerous systems, such as the environment, water resources, agriculture, industry, infrastructure, human health, and any other system that engages with the climate system, have faced negative impacts from these changes [1,2]. In South Asia, changes in precipitation patterns have resulted in flooding, droughts, and decreased yields of crops [2]. A better understanding of the regional changes in the projected precipitation is essential for the sustainable management of water resources, food security, and climate change adaptation [3].

General circulation models (GCMs) have been utilized extensively in climate change investigation, particularly after the Coupled Model Intercomparison Project (CMIP) began its operations [4,5,6]. These models play an important role in the study of climate change. The future projections of key climatic variables (precipitation and temperature) made using GCMs under different emission scenarios are highly valuable for understanding expected changes [7]. The latest phase (Phase-6) of the Coupled Model Intercomparison Project (CMIP6) has introduced multiple GCMs with improved spatiotemporal resolutions [8]. In the CMIP6’s sixth phase, future scenarios are made more realistic by combining Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). However, it is still challenging to analyze climate change at the regional and local scales using the GCMs of CMIP6 due to their coarse spatial resolutions [6]. Moreover, model parameterizations can also cause biases in the outputs of GCMs [9,10]. Due to the complex topography of the studied area, the projected precipitation and temperature data biases are more prevalent in mountainous areas. In order to accurately estimate the effects of climate change on water supplies and ecosystems, it is necessary to account for bias in the projections made using GCMs [11].

Generally, statistical and dynamic downscaling techniques are used before the direct use of GCM projections for a regional or local analysis of climate change. The statistical methods depend on data distribution and the correlation between the in situ and projected data during the historical period [12]. Dynamic downscaling techniques use regional climate models that are influenced by boundary conditions obtained from low-resolution GCMs [13]. The literature review of this study revealed that statistical and dynamical downscaling methods have inherent limitations [14]. The main limitation of dynamical downscaling is the requirement of substantial computational resources to operate regional climate models at higher spatial and temporal resolutions [15]. Moreover, the use of dynamic downscaling may not minimize the bias in climate variables, requiring bias corrections through statistical methods. Due to these limitations, statistical bias correction methods are commonly employed in climate change impact assessments [16,17]. These methods enhance the outputs of GCMs, enabling them to more accurately represent local climate conditions and increase the precision of projections in decision-making and developing climate adaptation strategies. The scientific community has come to consider bias correction as a viable method, leading to the development of multiple approaches for the bias correction of GCMs in recent years.

A variety of bias correction methods have been utilized in various studies. These methods include linear scaling (LS), the delta change (DC) method, mean bias correction, empirical quantile mapping (EQM), the mean and variance, adjusted quantile mapping (AQM), the trend-preserving quantile, and quantile delta mapping (QDM) [18,19,20]. The application of these techniques for bias correction results in a significant improvement in the quality of downscaling outputs. For instance, the extreme events represented in and the interannual variability of the GCM outputs are improved using a variance bias correction technique in China [21]. According to Gebrechorkos et al. [22], a statistical bias correction is a scale-based method that applies scale factors (e.g., LS and delta correction) to modify the results of GCM simulations. These factors can be either multiplicative or additive. More accurate global climate predictions could be made using the MME approach with mean-based bias correction [19,23].

Several studies have evaluated the performance of bias-corrected projections of GCMs in diverse topographic and climatic regions. For instance, Guo et al. [24] assessed the performances of 30 GCMs in simulating precipitation across Central Asia. In South Asia, Iqbal et al. [25] evaluated the performances of 35 GCMs. A recent study has assessed the accuracies of multiple GCMs across Pakistan [26] and recommended the use of the MIROC, CNRM-CM, BCC-CSM, and EC-EARTH models for climate change analysis across the country. Moreover, Ahmed et al. [27] evaluated the corrected projections of 36 GCMs which were used to simulate historical precipitation in Pakistan. They identified MIROC, BCC-CSM1-1, ACCESS1-3, and NorESM1-M as the most suitable GCMs for accurately replicating regional precipitation patterns. Latif et al. [28] assessed 36 GCMs in South Asia and recommended the HadGEM2-AO and CNRM-CM models for this region.

The CMIP6 has introduced five Shared Socioeconomic Pathways (SSPs) through which to analyze the potential changes in future climatic systems based on different levels of socioeconomic development and global cooperation [29]. Several studies have investigated the probable changes in the projected precipitation under different SSPs. For instance, Qiu et al. [30] analyzed the future precipitation changes in the Tibetan Plateau of China. They concluded that, under SSP5, precipitation would be expected to increase by 27% by the end of this century. Mishra et al. [31] also projected an increase in precipitation (about 20%) in South Asia under SSP5. These findings underscore the importance of investigating regional reactions to different SSPs, particularly in mountainous regions like northern Pakistan.

The northern highlands of Pakistan (NHP) are located in the western parts of the Himalayan Mountains, as indicated in Figure 1. This region of the Himalayas possesses distinctive climatic conditions, and glacier coverage, as well as a distinctive topology, ecology, and orientation. The NHP exhibit significant sensitivity to climatic changes, primarily due to their dependence on snowmelt and glacier-fed rivers for water [32]. Many prior climate change studies have neglected this mountainous region’s geographical limits. Consequently, there is an inadequate comprehension of the evolving trends in historical and future precipitation patterns, especially in the NHP. Comprehending these precipitation changes is crucial due to the significant implications involved. Alterations in precipitation patterns can profoundly affect agriculture, a sector that relies heavily on precipitation, as well as the generation of hydropower, which needs consistent water flow. Reduced summer precipitation may intensify water scarcity during essential growing periods, while an increasing variability could elevate the risk of flash floods, threatening infrastructure and human safety. These vulnerabilities necessitate precise projections that are essential for developing sustainable and effective climate resilience solutions. Recent changes in the seasonal precipitation patterns in mountainous regions of South Asia underline the need to conduct such evaluations, notably in the highlands of Pakistan. The cumulative consequences of global climate change are posing a growing threat to these highlands, which provide hydrological and climatic regulation for the area. Increasing temperatures and shifts in precipitation patterns have already resulted in modified river flow dynamics and an increased probability of extreme weather events, including floods and droughts [33,34,35,36]. Previous studies have predominantly concentrated on the upper Indus Basin and examined precipitation variations in the area under Representative Concentration Pathways (RCPs); however, a notable research deficiency exists in analyses of the precipitation under the most recent Shared Socioeconomic Pathways (SSP) scenarios. This study seeks to fill this gap by analyzing the anticipated annual and seasonal precipitation fluctuations in the NHP under the SSP2 and SSP5 scenarios, employing bias-corrected CMIP6 data. These results will contribute to the development of methods for adapting agricultural practices, managing water resources, and improving resilience to the effects of climate change.

This study seeks to examine the variations in the spatiotemporal patterns of the projected annual and seasonal precipitation in the NHP under the SSP2-4.5 and SSP5-8.5 scenarios. This study will offer comprehensive predictions by using bias-corrected projections from the CMIP6 GCMs, considering the region’s topography and climatic complexities. The findings of this study will be useful for better managing water resources, planning agricultural practices, and mitigating disaster risks. This study will provide important insights for decision-makers and community stakeholders as climate change changes hydrological regimes, supporting the improvement of resilience and the development of climate adaptation strategies in the NHP.

2. Study Area

The diversified landscape, distinct geographical location, and climate of the NHP affect the region’s environment and natural resources. The topography and geographical stretches of the NHP, between 33–37° N and 70–78° E, are shown in Figure 2. This region includes parts of the Hindu Kush, Karakoram, and Himalayan (HKH) mountains [37]. The elevation of the considered range varies between 184 and 8570 m a.m.s.l.

The NHP is distinguished by its cold and arid winter and hot and humid summer months [37]. The region receives significant precipitation and snowfall in the winter (from December to February), which helps replenish its water resources through snowmelt runoff [33]. The monsoon rains, which are essential for the region’s agricultural production and water supplies, are mainly experienced throughout the summer months (from June to September) [36]. The annual average precipitation in the study domain varies from 150 to 1700 mm/year. The summer season is the main rainy season in this region and accounts for about 53% of the total annual precipitation. Seasonal variations in the area’s precipitation are shown in Figure 3a. Western disturbances originating from the Mediterranean region bring precipitation during the winter and spring (from March to May). The autumn season (from October to November) accounts for the lowest amount of precipitation, about 6% of the total annual amount. This region significantly influences the surrounding area’s natural resources and environment. The precipitation in the NHP varies considerably (as shown in Figure 3b). Understanding the spatiotemporal trends and changes in precipitation events and their impacts on the region and its inhabitants is essential for effective management and planning.

3. Material and Methods

3.1. In-Situ Historical Data

This research examines the spatial and temporal patterns of precipitation change during the next three decades (2021–2050). Pakistan Meteorological Department (PMD) provided daily precipitation data from 23 stations, while the Water and Power Development Authority (WAPDA) of Pakistan provided data from 1 station (Bagh). The records have been verified as accurate and reliable by both the PMD and WAPDA. Moreover, several studies have used data from the selected weather stations for meteorological assessments [32,37,38,39,40]. Table 1 contains the details of the 24 weather stations that were considered for this study.

3.2. Future Projections of Global Circulation Models (GCMs)

In climate change assessments, the selection of an appropriate GCM is an important step, especially when investigating regions with a complicated topography, like the Northern Highlands of Pakistan. The selection of GCMs for this study was supported by a comprehensive evaluation of prior research assessing the efficacy of GCMs in replicating historical precipitation patterns and their capacity to represent the study region’s distinct meteorological attributes [24,26,41,42]. Some other factors, such as the model’s spatial resolution and representational accuracy, and the region’s availability of precipitation, maximum temperature, and minimum temperature under SSP2 and SSP5, were also considered in selecting appropriate GCMs. The previous investigations suggested some models that effectively describe seasonal variation, monsoonal patterns, and orographic precipitation events for this region. It is also important to note that the selected GCMs agree with models regularly utilized in regional climate impact studies. This ensures that they are consistent with the wider body of scientific literature. Due to the fact that no one GCM is capable of completely capturing all aspects of the climate system, we decided to use a multi-model ensemble (MME) method in order to take into account the variability that exists between models and to reduce the amount of uncertainty. This method makes the projections more reliable and ensures that our findings are reliable. The integration of bias correction enhances the models’ outputs, mitigating limitations encountered by the coarse resolutions of GCMs and enhancing the applicability of the projections for localized applications.

The simulated outputs of precipitation from five GCMs were obtained from the COPERNICOUS Website. These simulations were conducted under two different scenarios (SSP2-4.5, SSP5-8.5). Table 2 provides details of the considered GCMs.

3.3. Bias Correction of GCMs Projections

The downscaling or bias correction of GCM data is essential because they provide data at a coarse spatial resolution, which may not accurately capture detailed local or regional climate variations and patterns. By using bias-corrected GCM data, a more accurate depiction of these variations can be attained, allowing for a better understanding of the potential effects of climate change at the local scale [43]. Therefore, in this study, a statistical bias correction method called the delta change factor method (DCFM) was utilized. Future precipitation predictions have previously been adjusted using the delta change factor method, which involves applying a correction factor generated from the differences between predicted historical precipitation data and actual historical precipitation data. At first, the historical precipitation data of each GCM were compared with the predicted future precipitation under the SSP2-4.5 and SSP5-8.5 scenarios. After that, the historical precipitation data were subjected to the change factor, which is determined as the ratio or difference between the predicted and actual precipitation for each time step. Due to this approach, the observed historical patterns and the changes predicted by the GCMs were reflected in projected precipitation estimates. This approach allows for a consistent depiction of the precipitation’s temporal and spatial variability, considering annual and seasonal variations. This method is well known for its ease of use and good performance in reducing global circulation model (GCM) results to regional levels, especially for hydrological impact assessments. Previously, this method was successfully used for the bias correction of CMIP5 GCMs in the Swat River basin in northern Pakistan [6]. The projections of five GCMs for the projected timeframe (2021–2050) for two different scenarios, “SSP2-4.5” and “SSP5-8.5”, were downscaled. The following equations were used to calculate the change factor method:

(1)${CF}_{add}=\overline{P_f}-\overline{P_b}$

(2)${CF}_{mul}={\displaystyle \frac{\overline{P_f}}{\overline{P_b}}}$

where $\overline{P_b}$ and $\overline{P_f}$ represent the average precipitation during the baseline and future time frames, respectively. The local scale features (LSFs) were estimated using the following equations:

(3)${\mathrm{L}\mathrm{S}\mathrm{F}}_{\mathrm{a}\mathrm{d}\mathrm{d},\mathrm{i}}={\mathrm{O}\mathrm{b}\mathrm{s}}_{\mathrm{i}}+{\mathrm{C}\mathrm{F}}_{\mathrm{a}\mathrm{d}\mathrm{d}}$

(4)${\mathrm{L}\mathrm{S}\mathrm{F}}_{\mathrm{m}\mathrm{u}\mathrm{l},\mathrm{i}}={\mathrm{O}\mathrm{b}\mathrm{s}}_{\mathrm{i}}\times {\mathrm{C}\mathrm{F}}_{\mathrm{m}\mathrm{u}\mathrm{l}}$

where LSF_add,i and LSF_mul,i represent the adjusted estimates for the additive and multiplicative scenarios, respectively. For each time step (i) over the period, the observed precipitation data are denoted by Obs_i. Pb stands for the average precipitation from 1985 to 2014, and Pf for that of the future period.

3.4. Multi-Model Ensemble (MME)

The projected outputs of five GCMs were added to an ensemble using the following equation:

(5)$MME={\displaystyle \frac{1}{n}}\times \sum {(i=1)}^n{GCM}_i$

where n represents the count of GCMs used in the ensemble, GCMi indicates the simulated outputs of the ith GCM, and the symbol $\sum {(i=1)}^n$ shows the total number of the GCMs’ outputs. The MME, the average of all the GCM outputs, is widely used to predict future climatic conditions and is more accurate than any single model [29,33,44].

3.5. Trends Analysis

This study employed the non-parametric modified Mann-Kendall (MMK) test to examine the trends’ significance levels at a 95% level of confidence. This approach is especially well-suited for data that deviate from a normal distribution and are less affected by series outliers. The MMK test is a modified version of the Mann-Kendall test, which was first proposed by Hamed and Rao in 1998 [45]. The purpose of the MMK test is to compensate for the variance (S) of Mann-Kendall statistics. In order to determine the S, Z, variance V(S), and corrected variance V × (S), the following equations are utilized:

(6)$S=\sum _{j=1}^{n-1}\sum _{k=j+1}^{n}sgn(x_k-x_j)$

(7)$sgn(x_k-x_j)=\left\{\begin{array}{c}1 if x_k-x_j>0\\ 1 if x_k-x_j=0\\ 1 if x_k-x_j<0\end{array}\right\}$

(8)$V\left(S\right)=\left[n\left(n-1\right)\left(2n+5\right)-{\sum }_{t}9(t-1)(2t+5)/18\right]$

(9)$V\times \left(S\right)=V\left(S\right)Cor$

where

(10)$Cor=1+{\displaystyle \frac{2}{n(n-1)(n-2)}}\sum _{i=1}^{n-1}\left(n-1\right)\left(n-i-1\right)\left(n-i-2\right){\rho }_s\left(i\right)$

(11)$Z=\left\{\begin{array}{c}{\displaystyle \frac{s-1}{\sqrt{V}\left(S\right)}} if S>0\\ 0 if S=0\\ {\displaystyle \frac{s+1}{\sqrt{V}\left(S\right)}} if S<0\end{array}\right\}$

where $x_k$ and $x_j$ indicate the values at time points k and j (where k > j), n is the number of values, s(i) is the significant autocorrelation function of the ranks of the observations, and t is the time of duration. Positive or negative Z values indicate increasing or decreasing trends, respectively. The significance of the trends was assessed at the 95% significance level. For the assessment of the slope of the trend, the Theil–Sen (TS) slope estimator test was used, and the slope was calculated as follows:

(12)$T_i={\displaystyle \frac{X_j-X_k}{j-k}}$

In the above equation, X_j and X_k show the data values at time steps j and k, respectively.

(13)$Q=\left\{\begin{array}{c}{T}_{{\displaystyle \frac{N+1}{2}}} N is odd\\ {\displaystyle \frac{1}{2}}\left(T_{{\displaystyle \frac{N}{2}}}+T_{{\displaystyle \frac{N+2}{2}}}\right) N is even\end{array}\right\}$

4. Results

4.1. Temporal Distribution of Precipitation over the NHP

The average monthly precipitation for the NHP was estimated using the areal average estimate during the baseline period. The areal average data were estimated using the widely used Thiessen polygon method. The historical distribution of the monthly precipitation showed that the NHP receives the highest precipitation during July and August, followed by March. The results of the last thirty years’ monthly average precipitation and seasonal precipitation were obtained. November had the least precipitation (20.40 mm/month). The temporal variability of the monthly precipitation over the NHP is shown in Figure 4.

4.2. Temporal Trends of Historical Precipitation

The trends in the historical seasonal and annual total precipitation (1985–2014) were evaluated, as shown in Figure 5. The significance level (Z) and slope of each trend (Q) are also shown in Figure 5. During the historical period (baseline), it was found that the average annual precipitation decreased at the rate of 16.33 mm/decade. The magnitude of precipitation during the spring and autumn seasons was reduced at the rate of 20.80 and 1.60 mm/decade, respectively, while in the summer and winter seasons, it was slightly increased at 3.90 and 9.81 mm/decade, respectively. All seasonal and annual precipitation trends were statistically non-significant at the 95% significance level.

4.3. Temporal Trends of Projected Precipitation Under Two SSPs

Figure 6 compares the precipitation trends, during the historical period (baseline period: 1985–2014) and the future period (from 2021–2050), of annual precipitation under two SSPs. The projected outputs of the MME under SSP2–4.5 exhibited an increasing tendency of the annual average precipitation, with a rate of increase of 14.60 mm/decade. Similarly, the outputs of the MME under SSP5-8.5 showed a rising potential of future precipitation, at the rate of 12.70 mm/decade. However, no significant trend in the projected annual average precipitation was found under both SSPs.

4.4. Relative Changes in Future Precipitation

Figure 7 shows the relative changes in the projected seasonal and annual average precipitation amounts compared to those during the baseline period (1985–2014). The outputs of the MME suggested that the amounts of annual and seasonal precipitation are expected to decrease under both SSPs, except in the winter and autumn seasons, where SSP2-4.5 and SSP5-8.5, respectively, indicate increased precipitation. The average annual precipitation during the baseline period was 863 mm. In contrast, the annual average precipitation amounts for the future period under SSP2-4.5 and SSP5-8.5 were projected to be 842 mm (2.43% less than the baseline average) and 841 mm (2.54% less than the baseline average), respectively. Under SSP5-8.5, the future autumn precipitation is expected to increase by up to 32% compared to the baseline period.

4.5. Projected Trends of Annual and Seasonal Precipitation

The trends in the seasonal and annual precipitation amounts for the near future period (2021–2050) were analyzed. The significance levels (Z) and slopes of trends (Q) of the projected precipitation at different temporal scales are shown in Table 3. Under SSP2-4.5, four GCMs showed an increasing tendency, while one GCM showed a decreasing trend in the projected annual precipitation. According to the MME findings, the annual precipitation in the NHP is projected to increase by 14.6 mm/decade. All GCMs demonstrated a possibility of increase for total precipitation throughout the autumn season. The MME data indicated a rising trend in the anticipated precipitation throughout the autumn season at a rate of 7.2 mm/decade. Two GCMs revealed a decreasing trend in the summer precipitation, while three GCMs forecasted a reduced probability of precipitation in the spring season. The total amount of spring precipitation is expected to decrease at a rate of −4.2 mm/decade in the near future, as indicated by the result of the MME under SSP2-4.5. None of the seasonal and annual precipitation trends were statistically significant (at 95% significance level) under SSP2-4.5.

Under SSP5-8.5, an increasing trend in the projected seasonal and annual precipitation was found. Three GCMs exhibited a decreasing trend in the annual precipitation at rates ranging from −4.2 to −49.7 mm/decade. Conversely, the outputs of two GCMs revealed an increasing trend in the annual precipitation at rates ranging from 69.3 to 80.5 mm/decade. The annual average precipitation is expected to increase (12.7 mm/decade), as indicated by the result of the MME under SSP5. On the seasonal scale, the highest rate for a declining trend in precipitation was found for the winter season, with a rate of −3.0 mm/decade. Only one model showed significantly decreasing trends in the winter season. Overall, it is anticipated that the average precipitation of the winter and spring seasons will decrease in the near future, with the maximum decreasing trend rate (at −6.5 mm/decade) being in the spring season. In contrast, the average amount of precipitation during the summer and autumn seasons will increase.

4.6. Spatial Changes of Future Seasonal and Annual Precipitation

Figure 8 represents the spatial variations in the anticipated rates of change in the seasonal and annual precipitation. According to the projections under SSP2-4.5 for the near future (2021–2050), the amount of precipitation in the winter season may decrease over the southwestern parts of the study area. However, the central, northern, and southeast parts may receive higher precipitation amounts, with the potential rate of increase varying from 2.9 to 21.0 mm/decade. Conversely, the precipitation in the spring season may decrease at a rate ranging from −0.50 to −16.0 mm/decade across the study area. The southern and northern parts of the NHP are expected to receive more precipitation in the future. In the summer season, decreasing trends in precipitation are projected over the northern, western, and eastern parts of the study area, while increasing trends are expected over the southeastern parts. The amount of autumn precipitation is likely to increase across the entire study area, with a higher rate of increase in the southeastern parts (13.0 mm/decade). Overall, the annual precipitation exhibits an increasing trend over the north and south and a decreasing trend over the eastern and western parts. Under SSP2-4.5, the spatial distribution of precipitation changes generally follows a northeast-to-southwestern pattern across the different time scales.

Figure 9 illustrates the spatial distributions of the annual and seasonal precipitation change rates under SSP5-8.5. Under this scenario, winter precipitation is expected to decrease over the eastern, western, and southern parts of the study area. In contrast, a slightly increasing trend is expected over the northwestern parts of the study area. In the spring, the precipitation may decrease at rates ranging from −3.2 to −16.0 mm/decade across the entire study area except the eastern part. In contrast, a slightly increasing trend (up to 0.15 mm/decade) is predicted over the northeastern parts. In the summer season, the central parts to the southern and western parts of the study area are expected to receive more precipitation, with an increasing rate of 7.7–42.0 mm/decade, while, over all other parts, the precipitation may decrease. Autumn precipitation is projected to decrease slightly over the northeastern parts and may increase over the other parts of the study area, with rates varying between 3.7 and 19.0 mm/decade. Overall, the annual average precipitation is expected to decrease over most parts of the study area, except for the northwestern parts, where it may increase at a rate of 14–45 mm/decade.

5. Discussion

As a result of ongoing global warming, it is now crucial to estimate the changes in seasonal and annual precipitation for the sustainable management of water resources, hydropower production, and agricultural planning. The predicted shifts in the seasonality and amount of total annual precipitation are highly noticeable on the global and regional scales, particularly in mountainous regions. Due to the lack of in situ data in mountainous areas, most studies assessing climate trends and variability have focused on the national or international levels [2,42,46,47], emphasizing estimation of the mean attributes of the climate rather than focusing on trends and variability. In hilly, high-altitude areas with rough terrain, where the local climate is very modulated, the shifts in the seasonal and annual cycles of precipitation are more difficult to estimate [48]. This study investigated the spatiotemporal variabilities, trends, and magnitudes of annual and seasonal precipitation levels using daily time series of observed and predicted data for the northern highlands of Pakistan. In this work, we utilized historical data covering 30 years (from 1985–2014) from in situ stations and collected from the Pakistan Meteorological Department (PMD), and future projections from five GCMs of CMIP6 covering the period from 2021 to 2050. These two-time frames were considered to analyze the historical and future variations in the annual and seasonal precipitation cycles over the NHP. There is a distinct microclimate in this geographical/topographical area because of the wide variety of climatic settings.

Previous researchers recommended the GCMs used in this study [25,26,31]; therefore, this study did not evaluate the performance of the considered GCMs. The delta change factor approach was effectively utilized to reduce biases in the simulated outputs of the GCMs, and this approach was also used in previous research [49]. Numerous studies have emphasized the effectiveness of the multi-model ensemble technique in reducing uncertainty among individual models [50,51]; thus, considering projections obtained using the MME enhanced the accuracy and reliability of our predictions for the NHP.

The findings of this study provide important insights into the shifting patterns of precipitation across the northern highlands of Pakistan (NHP), a region that is essential for the availability of water resources and agricultural productivity. The region’s average annual precipitation decreased throughout the historical period (baseline), decreasing by 16.33 mm/decade. Usama et al. [37] also found a decreasing trend of annual total precipitation over the Himalayan range of Pakistan. In the spring, the precipitation decreased by 20.80 mm/decade, while in the fall, it decreased by 1.60 mm/decade, highlighting the drying trend. These findings align with the results of a recent study [52]. There can be serious consequences for farming and water supplies if spring precipitation decreases, as spring is a crucial planting season. Conversely, there was a little increase in summer (3.90 mm/decade) and winter (9.81 mm/decade) precipitation, which might provide a seasonal break but will probably not make up for the overall decrease in annual precipitation.

Compared to the historical period, the projections based on the multi-model ensemble (MME) method under both SSPs (SSP2-4.5 and SSP5-8.5) exhibit different patterns. While SSP5-8.5 predicts a much smaller increase in annual average precipitation, at 12.70 mm per decade, SSP2-4.5 predicts a rising trend, with a rise of 14.60 mm per decade. Compared to the historical period, the amounts of annual average precipitation decrease by 2.43% under SSP2-4.5 and 2.54% under SSP5-8.5, respectively, despite the overall increasing precipitation trend. This contradiction exemplifies the intricacy of future precipitation patterns, which are characterized by the fact that higher rates of increase during particular seasons do not completely compensate for long-term deficits.

In terms of the seasonal scale, the projections reveal that there are diverse seasonal patterns in the NHP. The annual precipitation is projected to rise by four GCMs under SSP2-4.5, while one GCM predicts a decline. According to all models, precipitation is anticipated to increase at a 7.2 mm/decade rate during autumn. On the other hand, the spring season is expected to experience the most significant decrease, with precipitation being reduced by 4.2 mm per decade. Similar patterns are seen under SSP5-8.5, with precipitation anticipated to decrease in the spring and winter and to increase by as much as 32% in autumn. Under SSP2-4.5, certain areas may witness an increase in winter precipitation, specifically in the region’s northern, central, and southeastern sections, and a decrease in spring and summer precipitation, respectively, compared to other locations.

The notable discrepancies in precipitation predictions under both SSPs highlight the challenges in formulating effective adaptation strategies for Pakistan. These discrepancies, particularly the significant decreases in precipitation and higher variability under SSP5-8.5, emphasize the importance of an adaptable, multi-scenario approach to mitigation planning. It is vital to optimize water-use efficiency, upgrade irrigation systems, and deploy drought-resistant crops immediately because both scenarios exhibit an expected decrease in summer precipitation. Concurrently, scenario-specific processes are required to help mitigate the various risks that are associated with each one. For instance, SSP5-8.5 may require greater investment in water storage infrastructure and flood management systems to alleviate severe droughts and significant precipitation events, whereas SSP2-4.5 may allow for a more moderate focus on incremental improvements. To strengthen resilience against extreme outcomes, policymakers should use SSP5-8.5 as a worst-case planning framework. They should also regularly revise their strategies in light of new knowledge and improved estimates. By employing adaptive management strategies that consider both commonalities and differences among scenarios, Pakistan can enhance its capacity to navigate uncertainty and alleviate the adverse impacts of climate change on water resources, agriculture, and regional development.

It is imperative to acknowledge the limitations of this investigation. In this study, we employed the delta change factor method for the bias correction of GCM data for local-scale application. This bias correction method is often utilized to modify GCM simulations to represent regional scales. Furthermore, it may maintain historical climate characteristics while predicting future variations. Nonetheless, this approach possesses some limitations. A primary limitation is the assumption of stationarity that the correlation between past and future precipitation patterns does not change. This assumption may inadequately account for potential changes in the frequency, severity, or spatial variability of extreme precipitation events under climate change. Moreover, the approach depends on GCM outputs, which provide information at coarse spatial resolutions, potentially neglecting fine-scale local effects such as orographic influences and microclimatic fluctuations, especially in areas with intricate terrain like the NHP. Although the bias correction implemented in this study mitigates certain errors, it may lack the capacity to fully address the problem of precisely modelling sub-grid-scale processes or sudden shifts in precipitation patterns.

Consequently, we employed the multi-model ensemble (MME) approach for this evaluation, which enhances the reliability of the obtained projections by mitigating uncertainty linked to individual models. The insights generated from this approach give useful information for the analysis of regional precipitation trends. These insights can also serve as a platform for future research that employs advanced downscaling approaches to accurately capture more localized dynamics.

6. Conclusions

This research investigated future (2021–2050) potential alterations in precipitation patterns over the study area compared to a historical baseline (1985–2014). Drawing solely from the outcomes of this study, the primary conclusions are outlined below:

• The average annual precipitation across the NHP has been consistently decreasing at a rate of −16.33 mm/decade, according to a trend analysis of historical precipitation (during 1985–2014). This finding underlines the importance of implementing water conservation strategies in order to ensure that the region’s food production is sustainable. The decreasing trend of precipitation over a significant ecological region is a major cause for concern for rain-fed ecosystems, food production, and populations.

• Both the spring and autumn seasons had considerable declines, while the spring season had a decline of 20.80 mm per decade. There is a possibility that crop yields and water supplies would suffer as a result of this decrease in spring precipitation, which constitutes a crucial stage for agricultural production.

• Based on the results obtained under both SSPs, it is anticipated that the annual average precipitation will be increasing in the near future, even if it declined during the baseline period. According to SSP2, the average annual precipitation across the NHP is projected to increase by 14.60 mm per decade, whereas SSP5 predicts a rise of 12.70 mm per decade. The estimated annual average precipitation is somewhat lower than the historical average since this increase is insufficient to completely compensate for the decreases from the baseline.

• The precipitation in autumn is anticipated to increase substantially in both scenarios, with the most pronounced increase (up to 32%) being seen under SSP5-8.5. This may lead to better chances for water storage and increased soil moisture, which might lessen the impact of there being less precipitation in subsequent seasons.

• Both scenarios predict that the spring precipitation will decrease at a rate of up to 6.5 mm per decade, which is the greatest decrease of any season. Similarly, it is anticipated that the winter precipitation will decrease in certain regions, which may impact the amount of water available during periods of drought.

• The spatial pattern of the predicted precipitation variations reveals considerable regional heterogeneity. Certain regions of the research area, especially in the north and southeast, may receive increased precipitation, whilst others are anticipated to undergo decreases. This inconsistency highlights the need for localized, adaptive water management systems to ensure water security.

As a result of regional variability and predicted changes in seasonal and annual precipitation, adaptive policies for sustainable water management are urgently needed in this region. More precipitation in autumn may have some positive effects, but future policies also need to consider the problems caused by decreases in precipitation in the spring and winter. In light of these findings, it is clear that water resource planning based on scientific evidence is essential for reducing the hazards associated with climate change in the NHP and that future climate consequences would be complicated.

Author Contributions

Conceptualization, M.A. (Muhammad Asif), M.N.A. and M.A. (Muhammad Azam); methodology, M.A. (Muhammad Asif), M.N.A., F.H. and A.A.; software, M.A. (Muhammad Azam), K.B.S. and W.I.; validation, S.J.M. and D.K.; formal analysis, M.A. (Muhammad Asif) and M.N.A.; investigation, D.K. and W.I.; resources, M.N.A. and D.K.; data curation, M.N.A., and W.I.; writing—original draft preparation, M.A. (Muhammad Azam), F.H. and A.A.; writing—review and editing, M.N.A., K.B.S., S.J.M. and D.K.; visualization, S.J.M., D.K. and W.I.; supervision, M.N.A. and D.K.; project administration, M.N.A.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data is contained within the article.

Acknowledgments

The Pakistan Meteorological Department (PMD) and the Water and Power Development Authority (WAPDA) of Pakistan are deeply appreciated for their crucial role in supplying us with daily precipitation measurements during the baseline period.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Location of the northern highlands of Pakistan (shaded) and the Hindu Kush, Karakoram, and Himalayan (HKH) Mountains.

Figure 2. Topographic map of the study area and locations of stations included in the study.

Figure 3. (a) Seasonal variations in precipitation over the NHP during 1985–2014. (b) Spatial distribution of annual average precipitation over the NHP from 1985 to 2014.

Figure 4. Monthly average precipitation in the NHP during 1985–2014.

Figure 5. Temporal trends of annual and seasonal precipitation in the NHP from 1985 to 2014. Z represents the test statistics of the MMK test (at 95% significance level), and Q represents the slope of the trend.

Figure 6. Comparisons of the future annual average precipitation amounts (from 2021–2050) under SSP2-4.5 and SSP5-8.5 with that for the baseline period (1985–2014).

Figure 7. The relative changes in average rainfall in the future (2021–2050) compared to the past (1985–2014).

Figure 8. Spatial distributions of the expected rates of change of future precipitation (for the period 2021–2050) for the winter (a), spring (b), summer (c), autumn (d), and annual (e) scales under SSP2-4.5 across the northern highlands of Pakistan.

Figure 9. Spatial distributions of the rates of change of future precipitation (for the period of 2021–2050) for the (a) winter, (b) spring, (c) summer, (d) autumn, and (e) annual scales across the northern highlands of Pakistan (under SSP5-8.5).

Table 1

Inventory of selected meteorological stations.

Sr. No.	Stations	Long (dd)	Lat (dd)	Altitude (m)	Average AnnualPrecipitation (mm)
1	Astore	74.9	35.37	2168	479.0
2	Bagh	74.09	33.78	2200	1247.0
3	Balakot	73.35	34.38	995.4	1496.9
4	Bunji	74.63	35.67	1470.0	166.0
5	Cherrat	71.55	33.81	1497.8	588.6
6	Chilas	74.10	35.42	1251.0	199.8
7	Chitral	71.83	35.85	1500	481.1
8	Dir	71.85	35.20	1375	1395.5
9	Drosh	71.78	35.57	1465	577.1
10	Garhi Dupatta	73.6	34.20	814	1351.4
11	Gilgit	74.33	35.92	1460	150.9
12	Gupis	73.41	36.17	2156	244.3
13	Islamabad	73.10	33.73	569	1206.8
14	Jhelum	73.36	32.94	287.2	863.8
15	Kakul	73.25	34.18	1309	1311.3
16	Kohat	71.43	33.57	513	555.9
17	Kotli	73.89	33.52	614	1192.9
18	Muzaffarabad	73.51	34.40	838	1426.5
19	Murree	73.41	33.91	2127	1725.3
20	Parachinar	70.08	33.86	1775	1021.0
21	Peshawar	71.56	34.02	360	504.7
22	Rawalpindi	73.04	33.59	540	1188.9
23	Saidu Shrif	72.35	34.82	970	1072.6
24	Skardu	75.54	35.34	2317	273.5

Table 2

Details of the CMIP6 GCMs used in this study.

Model Name	Institution	Resolution
ACCESS-CM2	“Commonwealth Scientific and Industrial Organization (CSIRO), Australia and Bureau of Meteorology (BOM)/Australia”	1.250° × 1.875°
BCC-CSM2-MR	“Beijing Climate Center, China Meteorological Administration, Beijing/China”	1.125° × 1.125°
CNRM-CM6-1	“European Research and Advanced Training in Scientific Computing, Toulouse/France”	1.0° × 1.0°
MIROC6	“The University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology/Japan”	1.4° × 1.4°
MPI-ESM1-2-LR	“Max-Planck-Institute for Meteorology/Germany”	1.5° × 1.5°

Table 3

Projected seasonal and annual precipitation trends over the NHP under SSP2-4.5 and SSP5-8.5. The bold values show statistically significant trends at 95% significance level.

GCM	Winter		Spring		Summer		Autumn		Annual
	Z	Q	Z	Q	Z	Q	Z	Q	Z	Q
SSP2-4.5
ACCESS-CM2	0.32	1.04	0.36	0.46	1.07	2.81	0.00	0.00	0.57	2.78
BCC-CSM2-MR	0.04	0.08	−1.46	−2.60	1.57	4.23	1.07	0.65	0.75	2.18
CNRM-CM6-1	−0.71	−1.61	−0.68	−1.64	−0.32	−1.55	0.86	0.66	−0.64	−4.26
MPI-ESM1-2-LR	0.68	1.03	−0.18	−0.40	−0.25	−1.36	1.57	0.78	0.21	3.73
MIROC6	0.00	−0.03	1.07	2.08	1.57	0.78	1.89	1.52	0.46	2.85
MME	0.06	0.10	−0.18	−0.42	0.73	0.98	1.08	0.72	0.27	1.46
SSP5-8.5
ACCESS-CM2	−1.11	−2.76	−1.36	−4.10	−0.39	−0.51	1.11	0.77	−0.82	−4.97
BCC-CSM2-MR	−1.96	−3.40	0.00	0.10	0.50	1.25	0.75	0.79	−0.75	−3.22
CNRM-CM6-1	1.43	2.48	0.36	1.28	1.14	4.74	2.60	2.98	1.43	8.05
MPI-ESM1-2-LR	1.53	3.93	−0.54	−1.24	0.71	3.43	−0.50	−0.42	0.96	6.93
MIROC6	−1.11	−1.76	0.89	0.70	−1.46	−3.58	0.29	0.14	−0.50	−0.42
MME	−0.24	−0.30	−0.13	−0.65	0.10	1.07	0.85	0.85	0.06	1.27

References

1. Islam, H.M.T.; Islam, A.R.M.T.; Abdullah-Al-Mahbub, M.; Shahid, S.; Tasnuva, A.; Kamruzzaman, M.; Hu, Z.; Elbeltagi, A.; Kabir, M.M.; Salam, M.A. et al. Spatiotemporal changes and modulations of extreme climatic indices in monsoon-dominated climate region linkage with large-scale atmospheric oscillation. Atmos. Res.; 2021; 264, 105840. [DOI: https://dx.doi.org/10.1016/j.atmosres.2021.105840]

2. Ul Hasson, S.; Pascale, S.; Lucarini, V.; Böhner, J. Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A Review of the CMIP5 climate models data for present climate and future climate projections. Atmos. Res.; 2016; 180, pp. 42-63. [DOI: https://dx.doi.org/10.1016/j.atmosres.2016.05.008]

3. Jafino, B.A.; Walsh, B.; Rozenberg, J.; Hallegatte, S. Revised Estimates of the Impact of Climate Change on Extreme Poverty by 2030. World Bank: Washington, DC, USA, 2020; [DOI: https://dx.doi.org/10.1596/1813-9450-9417]

4. Rasool, G.; Anjum, M.N.; Kim, D.Y.; Azam, M.; Hussain, F.; Afzal, A.; Maeng, S.J.; Min, K.C. Projecting climate change impact on precipitation patterns during different growth stages of rainfed wheat crop in the Pothwar Plateau, Pakistan. Climate; 2024; 12, 110. [DOI: https://dx.doi.org/10.3390/cli12080110]

5. Okkan, U.; Fistikoglu, O.; Ersoy, Z.B.; Noori, A.T. Investigating adaptive hedging policies for reservoir operation under climate change impacts. J. Hydrol.; 2023; 619, 129286. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2023.129286]

6. Anjum, M.N.; Ding, Y.; Shangguan, D. Simulation of the projected climate change impacts on the river flow regimes under CMIP5 RCP scenarios in the westerlies dominated belt, Northern Pakistan. Atmos. Res.; 2019; 227, pp. 233-248. [DOI: https://dx.doi.org/10.1016/j.atmosres.2019.05.017]

7. Song, J.N.; Fu, G.; Xu, Y.; Han, Z.Y.; Sun, Q.Z.; Wang, H. Assessment of the capability of CMIP6 global climate models to simulate arctic cyclones. Adv. Clim. Chang. Res.; 2021; 12, pp. 660-676. [DOI: https://dx.doi.org/10.1016/j.accre.2021.07.007]

8. Eyring, V.; Bony, S.; Meehl, G.A.; Senior, C.A.; Stevens, B.; Stouffer, R.J.; Taylor, K.E. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev.; 2016; 9, pp. 1937-1958. [DOI: https://dx.doi.org/10.5194/gmd-9-1937-2016]

9. Christensen, J.H.; Boberg, F.; Christensen, O.B.; Lucas-Picher, P. On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys. Res. Lett.; 2008; 35, L20709. [DOI: https://dx.doi.org/10.1029/2008GL035694]

10. Ashfaq, M.; Rastogi, D.; Mei, R.; Touma, D.; Ruby Leung, L. Sources of errors in the simulation of South Asian summer monsoon in the CMIP5 GCMs. Clim. Dyn.; 2017; 49, pp. 193-223. [DOI: https://dx.doi.org/10.1007/s00382-016-3337-7]

11. Pierce, D.W.; Cayan, D.R.; Maurer, E.P.; Abatzoglou, J.T.; Hegewisch, K.C. Improved bias correction techniques for hydrological simulations of climate change. J. Hydrometeorol.; 2015; 16, pp. 2421-2442. [DOI: https://dx.doi.org/10.1175/JHM-D-14-0236.1]

12. Thrasher, B.; Maurer, E.P.; McKellar, C.; Duffy, P.B. Technical note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci.; 2012; 16, pp. 3309-3314. [DOI: https://dx.doi.org/10.5194/hess-16-3309-2012]

13. Giorgi, F.; Gutowski, W.J. Regional dynamical downscaling and the CORDEX initiative. Annu. Rev. Environ. Resour.; 2015; 40, pp. 467-490. [DOI: https://dx.doi.org/10.1146/annurev-environ-102014-021217]

14. Abatzoglou, J.T.; Brown, T.J. A comparison of statistical downscaling methods suited for wildfire applications. Int. J. Climatol.; 2012; 32, pp. 772-780. [DOI: https://dx.doi.org/10.1002/joc.2312]

15. White, R.H.; Toumi, R. The limitations of bias correcting regional climate model inputs. Geophys. Res. Lett.; 2013; 40, pp. 2907-2912. [DOI: https://dx.doi.org/10.1002/grl.50612]

16. Xu, L.; Wang, A. Application of the bias correction and spatial downscaling algorithm on the temperature extremes from CMIP5 multimodel ensembles in China. Earth Sp. Sci.; 2019; 6, pp. 2508-2524. [DOI: https://dx.doi.org/10.1029/2019EA000995]

17. Gutmann, E.; Pruitt, T.; Clark, M.P.; Brekke, L.; Arnold, J.R.; Raff, D.A.; Rasmussen, R.M. An intercomparison of statistical downscaling methods used for water resource assessments in the United States. Water Resour. Res.; 2014; 50, pp. 7167-7186. [DOI: https://dx.doi.org/10.1002/2014WR015559]

18. Rocheta, E.; Evans, J.P.; Sharma, A. Can bias correction of regional climate model lateral boundary conditions improve low-frequency rainfall variability?. J. Clim.; 2017; 30, pp. 9785-9806. [DOI: https://dx.doi.org/10.1175/JCLI-D-16-0654.1]

19. Dai, A.; Rasmussen, R.M.; Ikeda, K.; Liu, C. A new approach to construct representative future forcing data for dynamic downscaling. Clim. Dyn.; 2020; 55, pp. 315-323. [DOI: https://dx.doi.org/10.1007/s00382-017-3708-8]

20. Tan, Y.; Guzman, S.M.; Dong, Z.; Tan, L. Selection of effective GCM bias correction methods and evaluation of hydrological response under future climate scenarios. Climate; 2020; 8, 108. [DOI: https://dx.doi.org/10.3390/cli8100108]

21. Xu, Z.; Yang, Z.L. An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. J. Clim.; 2012; 25, pp. 6271-6286. [DOI: https://dx.doi.org/10.1175/JCLI-D-12-00005.1]

22. Gebrechorkos, S.H.; Hülsmann, S.; Bernhofer, C. Statistically downscaled climate dataset for East Africa. Sci. Data; 2019; 6, pp. 2-9. [DOI: https://dx.doi.org/10.1038/s41597-019-0038-1]

23. Raju, K.S.; Kumar, D.N. Review of approaches for selection and ensembling of GCMS. J. Water Clim. Chang.; 2020; 11, pp. 577-599. [DOI: https://dx.doi.org/10.2166/wcc.2020.128]

24. Guo, H.; Bao, A.; Chen, T.; Zheng, G.; Wang, Y.; Jiang, L.; De Maeyer, P. Assessment of CMIP6 in simulating precipitation over arid Central Asia. Atmos. Res.; 2021; 252, 105451. [DOI: https://dx.doi.org/10.1016/j.atmosres.2021.105451]

25. Iqbal, Z.; Shahid, S.; Ahmed, K.; Ismail, T.; Ziarh, G.F.; Chung, E.S.; Wang, X. Evaluation of CMIP6 GCM rainfall in Mainland Southeast Asia. Atmos. Res.; 2021; 254, 105525. [DOI: https://dx.doi.org/10.1016/j.atmosres.2021.105525]

26. Iqbal, Z.; Shahid, S.; Ahmed, K.; Ismail, T.; Khan, N.; Virk, Z.T.; Johar, W. Evaluation of global climate models for precipitation projection in Sub-Himalaya region of Pakistan. Atmos. Res.; 2020; 245, 105061. [DOI: https://dx.doi.org/10.1016/j.atmosres.2020.105061]

27. Ahmed, K.; Sachindra, D.A.; Shahid, S.; Demirel, M.C.; Chung, E.S. Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrol. Earth Syst. Sci.; 2019; 23, pp. 4803-4824. [DOI: https://dx.doi.org/10.5194/hess-23-4803-2019]

28. Latif, M. Analysis of rainfall trends over Indo-Pakistan summer monsoon and related dynamics based on CMIP5 climate. Int. J. Climatol.; 2018; 38, pp. 577-595. [DOI: https://dx.doi.org/10.1002/joc.5391]

29. Ali, Z.; Hamed, M.M.; Muhammad, M.K.I.; Iqbal, Z.; Shahid, S. Performance evaluation of CMIP6 GCMs for the projections of precipitation extremes in Pakistan. Clim. Dyn.; 2023; 61, pp. 4717-4732. [DOI: https://dx.doi.org/10.1007/s00382-023-06831-6]

30. Qiu, H.; Zhou, T.; Zou, L.; Jiang, J.; Chen, X.; Hu, S. Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity. Atmos. Ocean. Sci. Lett.; 2024; 17, 100537. [DOI: https://dx.doi.org/10.1016/j.aosl.2024.100537]

31. Mishra, V.; Bhatia, U.; Tiwari, A.D. Bias-corrected climate projections for South Asia from coupled model intercomparison project-6. Sci. Data; 2020; 7, 338. [DOI: https://dx.doi.org/10.1038/s41597-020-00681-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33046709]

32. Nadeem, M.U.; Ghanim, A.A.J.; Anjum, M.N.; Shangguan, D.; Rasool, G.; Irfan, M.; Niazi, U.M.; Hassan, S. Multiscale ground validation of satellite and reanalysis precipitation products over diverse climatic and topographic conditions. Remote Sens.; 2022; 14, 4680. [DOI: https://dx.doi.org/10.3390/rs14184680]

33. Farhan, M.; Moazzam, U.; Rahman, G.; Munawar, S.; Tariq, A.; Safdar, Q.; Lee, B. Trends of rainfall variability and drought monitoring using standardized precipitation index in a scarcely gauged basin of Northern Pakistan. Water; 2022; 14, 1132. [DOI: https://dx.doi.org/10.3390/w14071132]

34. Ghanim, A.A.J.; Anjum, M.N.; Rasool, G.; Saifullah,; Irfan, M.; Alyami, M.; Rahman, S.; Niazi, U.M. Analyzing extreme temperature patterns in subtropical highlands climates: Implications for disaster risk reduction strategies. Sustainability; 2023; 15, 12753. [DOI: https://dx.doi.org/10.3390/su151712753]

35. Khan, N.; Shahid, S.; Ismail, T.B.; Wang, X.J. Spatial distribution of unidirectional trends in temperature and temperature extremes in Pakistan. Theor. Appl. Climatol.; 2019; 136, pp. 899-913. [DOI: https://dx.doi.org/10.1007/s00704-018-2520-7]

36. Saleem, F.; Zeng, X.; Hina, S.; Omer, A. Regional changes in extreme temperature records over Pakistan and their relation to pacific variability. Atmos. Res.; 2021; 250, 105407. [DOI: https://dx.doi.org/10.1016/j.atmosres.2020.105407]

37. Zafar, U.; Anjum, M.N.; Hussain, S.; Sultan, M.; Rasool, G.; Zain, M.; Riaz, B.; Shoaib, M.; Asif, M. Analyzing the spatiotemporal changes in climatic extremes in cold and mountainous environment: Insights from the Himalayan mountains of Pakistan. Atmosphere; 2024; 15, 1221. [DOI: https://dx.doi.org/10.3390/atmos15101221]

38. Ghanim, A.A.J.; Anjum, M.N.; Rasool, G.; Saifullah,; Irfan, M.; Rahman, S.; Mursal, S.N.F.; Niazi, U.M. Assessing spatiotemporal trends of total and extreme precipitation in a subtropical highland region: A climate perspective. PLoS ONE; 2023; 18, e0289570. [DOI: https://dx.doi.org/10.1371/journal.pone.0289570] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37540654]

39. Ahmad, I.; Zhang, F.; Tayyab, M.; Anjum, M.N.; Zaman, M.; Liu, J.; Farid, H.U.; Saddique, Q. Spatiotemporal analysis of precipitation variability in annual, seasonal and extreme values over upper Indus river basin. Atmos. Res.; 2018; 213, pp. 346-360. [DOI: https://dx.doi.org/10.1016/j.atmosres.2018.06.019]

40. Anjum, M.N.; Ding, Y.; Shangguan, D.; Ahmad, I.; Ijaz, M.W.; Farid, H.U.; Yagoub, Y.E.; Zaman, M.; Adnan, M. Performance evaluation of latest integrated multi-satellite retrievals for global precipitation measurement (IMERG) over the northern highlands of Pakistan. Atmos. Res.; 2018; 205, pp. 134-146. [DOI: https://dx.doi.org/10.1016/j.atmosres.2018.02.010]

41. Abbas, A.; Ullah, S.; Ullah, W.; Waseem, M.; Dou, X.; Zhao, C.; Karim, A.; Zhu, J.; Hagan, D.F.T.; Bhatti, A.S. et al. Evaluation and projection of precipitation in Pakistan using the coupled model intercomparison project phase 6 model simulations. Int. J. Climatol.; 2022; 42, pp. 6665-6684. [DOI: https://dx.doi.org/10.1002/joc.7602]

42. Abbas, A.; Bhatti, A.S.; Ullah, S.; Ullah, W.; Waseem, M.; Zhao, C.; Dou, X.; Ali, G. Projection of precipitation extremes over South Asia from CMIP6 GCMs. J. Arid Land; 2023; 15, pp. 274-296. [DOI: https://dx.doi.org/10.1007/s40333-023-0050-3]

43. Navarro-Racines, C.; Tarapues, J.; Thornton, P.; Jarvis, A.; Ramirez-Villegas, J. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data; 2020; 7, 7. [DOI: https://dx.doi.org/10.1038/s41597-019-0343-8] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31959765]

44. Wang, D.; Liu, J.; Luan, Q.; Shao, W.; Fu, X.; Wang, H.; Gu, Y. Projection of future precipitation change using CMIP6 multimodel ensemble based on fusion of multiple machine learning algorithms: A case in Hanjiang river basin, China. Meteorol. Appl.; 2023; 30, e2114. [DOI: https://dx.doi.org/10.1002/met.2144]

45. Hamed, K.H.; Rao, A.R. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol.; 1998; 204, pp. 182-196. [DOI: https://dx.doi.org/10.1016/S0022-1694(97)00125-X]

46. Arnell, N.W.; Lloyd-Hughes, B. The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Clim. Change; 2014; 122, pp. 127-140. [DOI: https://dx.doi.org/10.1007/s10584-013-0948-4]

47. Almazroui, M.; Saeed, S.; Saeed, F.; Islam, M.N.; Ismail, M. Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Syst. Environ.; 2020; 4, pp. 297-320. [DOI: https://dx.doi.org/10.1007/s41748-020-00157-7]

48. Yagbasan, O.; Demir, V.; Yazicigil, H. Trend analyses of meteorological variables and lake levels for two shallow lakes in Central Turkey. Water; 2020; 12, 414. [DOI: https://dx.doi.org/10.3390/w12020414]

49. Anandhi, A.; Frei, A.; Pierson, D.C.; Schneiderman, E.M.; Zion, M.S.; Lounsbury, D.; Matonse, A.H. Examination of change factor methodologies for climate change impact assessment. Water Resour. Res.; 2011; 47, 9104. [DOI: https://dx.doi.org/10.1029/2010WR009104]

50. Ghafouri-Azar, M.; Lee, S. Il Seasonal and monthly climate variability in South Korea’s river basins: Insights from a multi-model ensemble approach. Water; 2024; 16, 555. [DOI: https://dx.doi.org/10.3390/w16040555]

51. Huo, L.; Sha, J.; Wang, B.; Li, G.; Ma, Q.; Ding, Y. Revelation and projection of historic and future precipitation characteristics in the Haihe river basin, China. Water; 2023; 15, 3245. [DOI: https://dx.doi.org/10.3390/w15183245]

52. Hussain, A.; Cao, J.; Hussain, I.; Begum, S.; Akhtar, M.; Wu, X.; Guan, Y.; Zhou, J. Observed trends and variability of temperature and precipitation and their global teleconnections in the upper Indus basin, Hindukush-Karakoram-Himalaya. Atmosphere.; 2021; 12, 973. [DOI: https://dx.doi.org/10.3390/atmos12080973]