The high‐altitude Indus basin is one of the most complex and inadequately explored mountain terrains in the World, where reliable observations of precipitation are highly lacking. Therefore, spatially distributed precipitation products developed at global/regional scale are often used in several scientific disciplines. However, large uncertainties in precipitation estimates of such precipitation data sets often lead to suboptimal outcomes. In this study, performance of 27 widely used gridded precipitation products belonging to three different categories of gauge‐based, reanalysis and merged products is evaluated with respect to high‐quality reference climatologies of mean monthly precipitation. Widely used statistical measures and quantitative analysis techniques are used to analyse the spatial patterns and quantitative distribution of mean monthly, seasonal and annual precipitation at sub‐regional scale. Mean annual precipitation estimates of the gridded data sets are cross validated with the corresponding adjusted streamflows using Turc‐Budyko non‐dimensional analysis. Results reveal poor to moderately good performance of the gridded data sets. Marked differences in spatiotemporal and quantitative distribution of precipitation are found among the data sets. All data sets are consistent in their patterns showing negative or dry bias in wet areas and positive or wet bias in dry areas, although considerable differences in the magnitudes of the biases are noticed at sub‐regional scale. None of the data sets is equally good for all sub‐regions due to very high spatiotemporal variability in their performance at sub‐regional scale. Gauge‐based and merged products performed better in dry regions and during monsoon season, while reanalysis products provided better estimates in wet areas and during winter months. GPCC V8, ERA5 and MSWEP2.2 are found better than their counter‐grouped data sets. Overall, ERA5 is found most acceptable for all sub‐regions, particularly at higher‐altitudes, in wet areas and during winter months.

Air pollution is a serious public health issue in Pakistan’s metropolitan cities, including Lahore, Karachi, Faisalabad, Islamabad, and Rawalpindi. Pakistan’s urban areas are vulnerable due to air pollution drivers such as industrial activities, vehicular emissions, burning processes, emissions from brick kilns, urbanization, and other human activities that have resulted in significant human health issues. The purpose of this study was to examine the impact of air pollutants and smog, as well as their causes and effects on human health. The PRISMA technique was used to assess the impact of environmental contaminants on human health. This study looked at air pollution sources and pollutants such as PM_2.5, PM₁₀, CO₂, CO, SO_X, and NO_x from waste combustion and agriculture. The population included people of all ages and sexes from both urban and rural areas of Pakistan. Data were retrieved and analyzed using SRDR⁺ software and Microsoft Excel spreadsheets. The data suggested that Karachi and Lahore had the highest levels of air pollution and disease prevalence, which were attributed to heavy industrial activity and traffic emissions. Smog was a serious concern in Lahore during winter, contributing to the spread of several diseases. Other cities, including Islamabad, Rawalpindi, Jhang, Sialkot, Faisalabad, and Kallar Kahar, were impacted by agricultural operations, industrial pollutants, brick kilns, and urbanization. Due to these drivers of air pollution, some diseases such as respiratory and cardiovascular diseases had notably higher incidences in these cities. Other diseases were connected with air pollution exposure, asthma, eye and throat problems, allergies, lung cancer, morbidities, and mortalities. To reduce air pollution’s health effects, policies should focus on reducing emissions, supporting cleaner technologies, and increasing air quality monitoring.

We estimate future changes in precipitation over the entire Indus basin of Pakistan with a particular focus on the high‐elevation upper indus basin (UIB). A statistical downscaling approach is used. We consider the spatial variability of observed precipitation on seasonal scales. Large‐scale atmospheric patterns are employed for general circulation model (GCM) selection and subsequent projections. Firstly, we identify the precipitation governing predictors from ERA‐Interim reanalysis. We further quantify the robustness of governing predictors against other reanalysis datasets (ERA5 and NCEP‐NCAR‐II) to assess future projections' fidelity. We perform S‐mode Principal Component Analysis on predictor fields and compare loading patterns using Taylor diagrams to assess predictor correspondence between different reanalysis. Similarly, we compare ERA‐Interim variables with model‐simulated fields during the historical period to select better performing GCMs and quantify model uncertainty. The regional suitability of available GCMs in our study is also demonstrated. Ensemble (median) changes in regional precipitation derived through atmospheric fields show an elevation‐dependent response of the UIB at representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, where increased precipitation will mostly fall at high elevations. However, the positive signals are more distinct during the winter and monsoon seasons, particularly over the central Karakoram. Meanwhile, a decrease in precipitation is robust during the pre‐monsoon period, particularly over the northwestern regions. These signals intensify and become more robust during 2071–2100 under RCP8.5, and the better‐performing models and signal‐to‐noise ratios further support this finding. The spatial patterns of projected changes suggest stronger (weaker) and further northward penetrating westerly systems during the winter (pre‐monsoon) season. Increased warming will also strengthen monsoon circulations, and these will penetrate further into the northwestern and trans‐Himalayan regions. The Lower Indus shows a mixed seasonal response that is more uncertain. The present analysis provides an alternative perspective to the ongoing research of assessing climate responses in complex regions.

We assessed maximum (T_max) and minimum (T_min) temperatures over Pakistan’s Indus basin during the 21st century using statistical downscaling. A particular focus was given to spatiotemporal heterogeneity, reference and General Circulation Model (GCM) uncertainties, and statistical skills of regression models using an observational profile that could significantly be improved by recent high-altitude observatories. First, we characterized the basin into homogeneous climate regions using K-means clustering. Predictors from ERA-Interim reanalysis were then used to model observed temperatures skillfully and quantify reference and GCM uncertainties. Thermodynamical (dynamical) variables mainly governed reference (GCM) uncertainties. The GCM predictors under RCP4.5 and RCP8.5 scenarios were used as “new” predictors in statistical models to project ensemble temperature changes. Our analysis projected non-uniform warming but could not validate elevation-dependent warming (EDW) at the basin scale. We obtained more significant warming during the westerly-dominated seasons, with maximum heating during the winter season through T_min changes. The most striking feature is a low-warming monsoon (with the possibility of no change to slight cooling) over the Upper Indus Basin (UIB). Therefore, the likelihood of continuing the anomalous UIB behavior during the primary melt season may not entirely be ruled out at the end of the 21st century under RCP8.5.

Complex processes govern spatiotemporal distribution of precipitation within the high-mountainous headwater regions (commonly known as the upper Indus basin (UIB)), of the Indus River basin of Pakistan. Reliable precipitation simulations particularly over the UIB present a major scientific challenge due to regional complexity and inadequate observational coverage. Here, we present a statistical downscaling approach to model observed precipitation of the entire Indus basin, with a focus on UIB within available data constraints. Taking advantage of recent high altitude (HA) observatories, we perform precipitation regionalization using K-means cluster analysis to demonstrate effectiveness of low-altitude stations to provide useful precipitation inferences over more uncertain and hydrologically important HA of the UIB. We further employ generalized linear models (GLM) with gamma and Tweedie distributions to identify major dynamic and thermodynamic drivers from a reanalysis dataset within a robust cross-validation framework that explain observed spatiotemporal precipitation patterns across the Indus basin. Final statistical models demonstrate higher predictability to resolve precipitation variability over wetter southern Himalayans and different lower Indus regions, by mainly using different dynamic predictors. The modeling framework also shows an adequate performance over more complex and uncertain trans-Himalayans and the northwestern regions of the UIB, particularly during the seasons dominated by the westerly circulations. However, the cryosphere-dominated trans-Himalayan regions, which largely govern the basin hydrology, require relatively complex models that contain dynamic and thermodynamic circulations. We also analyzed relevant atmospheric circulations during precipitation anomalies over the UIB, to evaluate physical consistency of the statistical models, as an additional measure of reliability. Overall, our results suggest that such circulation-based statistical downscaling has the potential to improve our understanding towards distinct features of the regional-scale precipitation across the upper and lower Indus basin. Such understanding should help to assess the response of this complex, data-scarce, and climate-sensitive river basin amid future climatic changes, to serve communal and scientific interests.

The Indus River system originates within high mountain ranges of Hindukush, Karakoram and Himalayans (HKH) and contains the largest cryosphere outside the Polar Regions. It assures livelihood of millions of people, before descending into the Arabian Sea. Different processes, which involve complex interplays of contrasting synoptic-scale circulations and regional topography, largely govern precipitation, which varies significantly with space-time and altitudes in upper Indus basin (UIB). In contrast, the Lower Indus (LI) has arid to semi-arid climate and depends heavily on melt-dominated water supply from the UIB. Considering climate hotspot nature of this basin, a pragmatic assessment of future precipitation and temperature changes at basin-scale are fundamental to provide effective policy advice. However, long-term, reliable and consistent data to effectively simulate orographic climatology within UIB that largely governs the basin hydrology is scarce. Consequently, even the mean direction of regional climate is highly controversial and ranging from rapidly retreating glaciers to the so-called “Karakoram anomaly”. While the provision of additional useful data is still an ongoing process, improvements in simulation methodologies using the available observational network, can still offer some opportunities to reduce uncertainties. One way is to make use of large-scale atmospheric circulations, which are modeled more reliably than precipitation itself. Moreover, the circulation-precipitation relationships can additionally explain governing mechanisms to improve confidence in resulting simulations. In our study, we modeled observed precipitation and temperature (Tmax and Tmin) dynamics of the entire basin. A seasonally and spatially differentiated analysis was done using improved UIB monitoring, which provide enhanced spatio-altitudinal information. By taking advantage of the recent high-altitudes (HA) installations within UIB, we argue that precipitation at relatively low-altitudes only quantitatively differ from HA rates, but share a significant joint variability at sub-regional scales. Therefore, the low-altitude stations (historic) can provide reasonable inferences about more uncertain orographic structure of UIB. We adapted generalized linear models (GLMs) with Tweedie and Gamma distributions to model precipitation and multiple linear regressions (MLRs) for temperature simulations using time-series of carefully selected regionally representatives, as predictand and principal component scores of different larger-scale dynamical and thermodynamic variables from ERA-Interim reanalysis, as predictors. The final regression models, which were identified through a cross validation framework, showed significant statistical skills and physical consistency to simulate observed seasonal precipitation and temperature variability over larger spatio-altitudinal scales.     We further used the predictors to identify better performing regional and seasonal CIMP5- GCMs by comparing predictors through Taylor diagrams in the historical period. ERA-Interim predictors served as a basis for evaluation. Reanalysis uncertainties were assessed by using also NCEP-NCAR-II and ERA5 reanalysis. We considered two radiative forcings (RCP4.5 and RCP8.5) to analyze median change signals of precipitation (temperature) during mid (2041-2070) and end of 21st century (2071-2100). The signal to noise ratio was computed to evaluate future changes compared to observed natural variability.