Multivariate Climate Diagnostics and CMIP6 SSP245-Driven Future Pathways for Chhedagad Municipality, Jajarkot, Nepal (1984-2050)

Multivariate Climate Diagnostics and CMIP6 SSP245-Driven Future Pathways for Chhedagad Municipality, Jajarkot, Nepal (1984-2050) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multivariate Climate Diagnostics and CMIP6 SSP245-Driven Future Pathways for Chhedagad Municipality, Jajarkot, Nepal (1984-2050) Ganesh Rawat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8570798/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Anticipating future hydro-meteorological risks under a warming climate requires an understanding of localized climate behavior in remote Himalayan municipalities. Using 40 years of satellite-derived observations (1984–2024) from NASA POWER and mid-century projections (2025–2050) from downscaled CMIP6 SSP2-4.5 data, this study offers the first thorough multivariate climate assessment for Chhedagad Municipality, Jajarkot. Historical analyses reveal a clear warming tendency across all 13 wards, strong elevation-dependent temperature gradients, and a post-2000 intensification of precipitation accompanied by heightened interannual variability. Humidity has increased steadily, while solar radiation and wind speeds show declining trends since the early 2000s, indicating shifts in atmospheric moisture, cloudiness, and circulation. A highly coherent climate system dominated by monsoon processes is revealed by multivariate correlation diagnostics. The amount of clouds has a significant influence on temperature and solar radiation, humidity is correlated with precipitation, and all variables show spatially uniform behavior across wards. Forecasts for the middle of the century show persistent variability in radiation and wind regimes, higher atmospheric moisture, sustained monsoonal enhancement, and ongoing warming. When taken as a whole, these findings show a shift toward a climate that is warmer, wetter, and more moisture-rich, with more hydro-climatic uncertainty and stronger seasonal contrasts. In addition to providing practical insights for climate-resilient planning in agriculture, water resources, energy, and hazard management, this study creates the first localized climate baseline for Chhedagad. Climatology Meteorology Climate Analysis and Modeling Hydrology Chhedagad Municipality Multivariate Climate Diagnostics NASA POWER CMIP6 SSP2-4.5 Himalayan Climate Future Projections Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 1. Introduction Recent climate research across the Himalayan region consistently shows strong warming signals and increasing hydro-climatic variability, underscoring the need for localized climate diagnostics and high-resolution future projections. Studies such as (Sigdel et al., 2022 ) demonstrate significant historical increases in both precipitation and temperature across mountainous basins, with CMIP6 models projecting further intensification of monsoon rainfall and a pronounced rise in Tmin under intermediate and high-emission pathways. These findings align with broader regional assessments indicating rising warm extremes, declining winter precipitation, and increasing frequency of heavy rainfall events, factors that heighten risks of floods, landslides, droughts, and glacier-related hazards in Nepal’s mid- and far-western districts. Despite these advances, fine-scale multivariate analyses that integrate temperature, precipitation, cloud dynamics, humidity, wind behaviour, and solar-energy patterns remain limited for remote municipalities such as Chhedagad in Jajarkot. This gap highlights the importance of the present study, which uses observed climatology (1984–2024) together with CMIP6 SSP2-4.5 projections to provide a comprehensive diagnostic of multi-parameter climate behaviour and future pathways relevant for agriculture, water resources, and climate-hazard preparedness in Chhedagad Municipality. Research on high-altitude climate and hydrological systems in Jajarkot has begun to highlight the strong influence of climate variability on local environmental processes. (Rawat et al., 2025 ) showed that the trio of glacial lakes in Barekot Patan is undergoing noticeable morphological changes driven by long-term glacier retreat, increased snowmelt, and shifting precipitation patterns, emphasizing rising GLOF-related risks in the region. Building on this regional understanding, (Rawat, 2025 ) further demonstrated that hydro-meteorological conditions in nearby Nalphu Village exhibit clear multi-decadal variability, including a statistically significant cooling trend after the early 2000s and a strong shift toward wetter monsoon conditions, with machine-learning forecasts indicating continued moisture-rich atmospheric behavior and moderate warming through 2050. Together, these studies highlight how climate-driven changes in temperature, precipitation, and cryospheric processes are reshaping both hydrological regimes and high-altitude landscapes of Jajarkot, underscoring the need for localized climate diagnostics and adaptation planning. Climate research in Nepal shows consistent warming and shifting precipitation patterns, with rising extremes due to complex topography and limited observational coverage. Studies highlight that CMIP6 models provide improved climate projections, though bias correction, especially quantile mapping, is essential for reducing model uncertainty and producing reliable local estimates (Joshi & Dhital, 2023 ). similarly reports strong future increases in temperature and precipitation across Nepal, particularly under high-emission scenarios, reinforcing earlier evidence of heightened climate vulnerability in regions like Karnali and the central hills. Overall, existing literature agrees that Nepal is moving toward warmer and wetter conditions, underscoring the need for localized climate diagnostics and future pathway assessments. Recent studies across Nepal and the Himalayan region consistently report significant warming and increasing precipitation variability, driven by the region’s strong elevation-dependent climatic response. CMIP6 models have shown improved capability in representing these patterns, especially in complex basins such as Karnali, where bias correction substantially enhances model realism. The uploaded study by (Lamichhane et al., 2024 ) demonstrates clear future increases in precipitation and temperature across the basin, alongside marked changes in streamflow and baseflow patterns, emphasizing rising climate-induced hydrological stress. Similar research highlights that minimum temperatures are rising faster than maximum temperatures, and monsoon intensification is increasingly evident across western Nepal. Collectively, the literature underscores Nepal’s high climate sensitivity and the need for localized diagnostics and future projections to support adaptation planning. Recent climate studies in Nepal show clear warming trends and increasing precipitation extremes, especially in high-altitude regions. CMIP6-based research provides improved future projections, but downscaling is critical due to sparse meteorological data. The uploaded study confirms strong increases in extreme rainfall and temperature indices across all SSP scenarios for Nepal (Bastola et al., 2024 ). Overall, existing literature agrees that Nepal is moving toward a warmer and wetter climate with more intense extremes, highlighting the need for localized climate assessments. Climate studies across Nepal consistently show rising temperatures and increasing precipitation extremes, especially in mountain basins. Recent CMIP6-based research provides improved projections, highlighting significant warming and stronger monsoon intensification under higher-emission pathways. The uploaded Tamor Basin study also reports clear increases in temperature and precipitation across SSPs, reinforcing Nepal’s growing hydro-climatic vulnerability (Subedi et al., 2024 ). Together, the literature underscores an urgent need for localized climate diagnostics to support adaptation planning. According to (Dahal et al., 2020 ), the study shows clear evidence of increasing temperature trends and shifting precipitation patterns across Nepal’s diverse climatic zones. Their analysis highlights a rising frequency of hydro-climatic extremes, driven by both seasonal variability and long-term warming signals. He further emphasize that these changes intensify local vulnerability, demonstrating the need for detailed, municipality-scale climate assessments such as the present research. (Ullah et al., 2020 ) evaluated 21 CMIP5 models and showed that South Asia is experiencing consistent warming, with Tmin, Tmax, and Tmean all increasing under 1.5°C, 2°C, and 3°C global warming thresholds. Their results indicate that warming intensifies toward higher latitudes, especially across the Hindu Kush–Himalayan region, where temperature extremes are projected to strengthen rapidly. Ullah et al. further demonstrated that warming arrives earlier in South Asia than the global average, highlighting high regional vulnerability. This study provides strong evidence that future climate conditions will amplify thermal extremes and increase hydro-climatic risks across the region. (Li et al., 2021 ) showed that CMIP6 models generally reproduce the spatial and seasonal climate patterns of the Third Pole but still retain substantial cold and wet biases, especially over monsoon-dominated regions. Their evaluation further demonstrated that future warming in these high-altitude areas is projected to exceed global averages, with stronger wetting trends in summer for monsoon-influenced zones. Li et al. also emphasized persistent model uncertainties due to complex topography and circulation interactions, underscoring the need for localized climate analyses such as the present study. (Saha et al., 2025 ) developed high-resolution, bias-corrected CMIP6 datasets to improve ETo estimation across South Asia, addressing major warm, radiation, and humidity biases found in raw model outputs. Their results show that quantile-mapping correction substantially enhances agreement with ERA5 observations, making the datasets suitable for local climate-impact analysis. Projected ETo increases, especially under higher-emission scenarios, indicate growing water-stress risks across regions such as Afghanistan and northern India. Overall, the study provides a robust foundation for reliable hydrological and agricultural planning under future climate change. Glacier change assessments in the Upper Indus Basin reveal rapid mass loss and substantial area reduction, as demonstrated by (Abdullah et al., 2025 ) who show that warming under SSP245 and SSP585 will significantly accelerate glacier retreat. Their results indicate that even modest precipitation increases cannot offset the strong temperature-driven melt, leading to projected deglaciation exceeding 55% by the 2080s. According to him these changes pose serious implications for future water availability, hydropower, and agricultural demand in the region. Overall, the attached study provides clear evidence of severe cryospheric vulnerability under ongoing climate change. Recent assessments show that the Southern Himalayas face some of the world’s most severe heat-stress risks, with WBGT levels frequently exceeding safe thresholds, as demonstrated by (Yang et al., 2024 ). Their analysis reveals rapidly rising hours in Categories 3–5, indicating intensifying impacts on outdoor labor and human health. The study further shows that under SSP2-4.5 and SSP5-8.5 scenarios, extreme heat events similar to 2020 will become common, exposing millions to dangerous heat stress. According to Yang et al., climate change, rather than atmospheric circulation, is the primary driver of this escalating risk. (Karimzadeh et al., 2025 ) rapid warming across the Tibetan Plateau is driving significant shifts in atmospheric heat sources, altering monsoon strength and regional moisture transport. Their analysis shows that heat-source intensity has strengthened in recent decades, leading to enhanced land-atmosphere coupling and greater climate sensitivity in downstream regions. He further note that these changes intensify both summer precipitation variability and extreme climate responses across Asia, highlighting the Plateau’s growing influence on broader monsoon dynamics. Severe heat events across South Asia have intensified over recent decades, with (Colston et al., 2018 ) showing that wet-bulb temperatures are approaching physiological limits under continued warming. Their analysis demonstrates that both dry-bulb and humidity-driven heat stress will sharply increase under high-emission scenarios, making previously rare extremes more frequent and more dangerous. (Dhakal et al., 2020 ) demonstrated that empirical temperature-based equations and machine-learning models can effectively estimate evapotranspiration across Nepal when conventional FAO-56 inputs are limited, highlighting the strong sensitivity of ETo to local climatic variability and the advantages of data-driven techniques in mountainous environments. Building on this, (Shrestha et al., 2025 ) showed that advanced ML models such as XGBoost, DNN, and LSTM consistently outperform traditional empirical methods when predicting ETo across Nepal’s diverse agro-meteorological zones, with radiation-based inputs yielding the highest accuracy and station clustering further reducing prediction error. Recent advancements in hydro-climatic modeling across Nepal highlight the growing reliance on data-driven and physically based approaches to understand climate variability and its sectoral impacts. Temperature-based empirical and machine-learning techniques have been shown by (Dhakal et al., 2020 ) to greatly improve evapotranspiration estimation under limited-data conditions. At the basin scale, (Jha et al., 2025 ) used a multi-site SWAT framework with CMIP6 projections to show that future hydrology in the Koshi Basin will experience increased discharge and enhanced hydropower potential, particularly when adaptive design discharges are applied. Complementing this, (Pradhananga et al., 2025 ) found that the Karnali Basin will face pronounced seasonal shifts, with monsoon flows increasing and winter–spring flows declining, which in turn reduces dry-season energy reliability and increases irrigation water demand. Collectively, these studies highlight the critical need for localized, high-resolution climate diagnostics and integrated modeling frameworks to support resilient water, energy, and agricultural planning under accelerating climate change. Chhedagad Municipality remains critically understudied, with no dedicated long-term climate assessments despite its high sensitivity to temperature shifts, monsoon irregularities, and increasing climate-driven hazards; the absence of local stations and scientific investigations has created a major research gap in understanding multivariate climate behavior in this remote Himalayan region. To address this gap, the study aims to analyze four decades of historical climate variability, diagnose interrelationships among key climatic variables, identify long-term trends and shifts in temperature, precipitation, solar radiation, humidity, wind, and cloud cover, and finally develop CMIP6 SSP245-driven climate pathways projecting conditions through 2050. The main objective is to generate a localized, scientifically robust climate profile that supports evidence-based adaptation planning for communities in Chhedagad. 2. Data and methods 2.1 Study Area Chhedagad Municipality is situated in the south-central part of Jajarkot District within Karnali Province, Nepal, covering an area of approximately 284.20 km ² and accommodating a population of 37,877 according to the 2021 national census ( Population | National Population and Housing Census 2021 Results , n.d.). Administratively, it comprises 13 wards, formed after the nationwide restructuring of 2017 through the merger of the former Salma, Dasera, Suwanauli, Pajaru, Jhapra, and Karkigaun Village Development Committees, with Karkigaun designated as the municipal headquarters.( Chhedagad Municipality, Karkigaun, Jajarkot, Karnali Province, Nepal. | “Prosperous Chhedagad, Our Future,” n.d.) Geographically, the municipality lies within the mid-hill physiographic belt of the Himalayas, characterized by rugged terrain, steep slopes, variable elevations, and deeply incised river valleys typical of Jajarkot District ( Jajarkot Topographic Map, Elevation, Terrain , n.d.). This landscape is influenced by the Mahabharat Range and contributes to pronounced micro-climatic variation, where temperature, precipitation, and other meteorological parameters fluctuate across short spatial distances. The municipality is bordered by Junichande Rural Municipality to the north and northwest, Barekot and Kuse areas toward the central and eastern highlands of Jajarkot, and Simta and Bheri regions toward the south, situating Chhedagad as a transitional zone between higher Himalayan ridges and warmer southern valleys, as shown in the attached map. The study area represents a typical mid-hill settlement dependent on agriculture, rainfall variability, and natural water systems. Because of limited meteorological stations and its rugged topography, climate information for Chhedagad relies largely on gridded and satellite-derived datasets such as NASA POWER and CMIP6 products. These conditions make the municipality an important site for assessing long-term hydro-meteorological variability, understanding local climatic sensitivities, and projecting future changes under warming scenarios 2.2 Data Sources This study relies on a combination of observational climate datasets and global climate model simulations to examine multivariate hydro-meteorological behavior in Chhedagad Municipality. Historical climate data for the period 1984–2024 was obtained from the NASA POWER database ( NASA POWER | Data Access Viewer (DAV) , n.d.), which provides satellite-derived, bias-adjusted meteorological parameters with global consistency. These data are particularly valuable for areas such as Jajarkot District, where ground-based climate stations are sparse and long-term observational records are limited. The variables extracted from NASA POWER include monthly mean, maximum, and minimum air temperature, total precipitation, relative and specific humidity, surface pressure, solar radiation, cloud amount, and wind speed. These variables collectively represent the principal drivers of local climate processes and provide a comprehensive basis for multivariate climate diagnostics. To explore future climate pathways, projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the SSP2-4.5 (SSP245) scenario were incorporated into the analysis. Downscaled and bias-corrected model outputs available through the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) archive were used to obtain future monthly climate data at a spatial resolution of 0.25°. This product integrates bias correction and statistical downscaling, allowing for improved representation of climate characteristics in complex mountainous terrain. Only the grid cells corresponding to the centroid and spatial footprint of Chhedagad Municipality were selected to generate a future climate series for 2025–2050. The SSP245 scenario, representing a stabilization pathway with moderate emissions, was chosen because of its relevance to near- and mid-century regional climate planning. 2.3 Data Preprocessing and Quality Control Before analysis, all datasets underwent a structured preprocessing workflow to ensure temporal consistency and analytical reliability. Raw CSV files were inspected for missing values, formatting inconsistencies, and non-numeric entries, which were corrected or removed as necessary. Column names were standardized, and the month-wise datasets were reshaped into continuous time-series formats suitable for long-term trend assessment. The year 1983 was excluded because several variables contained incomplete or unreliable entries, and the analysis period was therefore defined as 1984–2024. From the cleaned dataset, monthly, seasonal, and annual means were computed, and a climatological baseline spanning 1984–2014 was established to evaluate anomalies. To ensure comparability between observational datasets and future climate projections, CMIP6 SSP245 model outputs were harmonized with the NASA POWER observations. This adjustment preserved long-term trends while aligning the model’s mean state with observed climatic conditions. The harmonized dataset enabled the construction of a continuous historical–future climate series for all key hydro-meteorological variables. All processing steps were conducted using Python libraries such as Pandas, NumPy, and xarray, ensuring reproducibility and transparency. 2.4 Trend Analysis and Multivariate Climate Diagnostics Long-term climate behavior in Chhedagad Municipality was examined through a combination of statistical trend analysis and multivariate diagnostic techniques. Historical patterns were characterized by computing anomalies relative to the climatological baseline, allowing identification of long-term warming, cooling, wetting, drying, and humidity-related shifts over the study period. To detect monotonic changes in meteorological variables, the non-parametric Mann-Kendall test was applied, while the Sen’s slope estimator quantified the magnitude of change per year. These methods enabled the evaluation of gradual long-term trends without assuming linearity or normally distributed data, making them appropriate for the climatic context of mountainous Nepal. In addition to trend assessment, interconnections among climatic variables were examined to understand how temperature, precipitation, humidity, cloud cover, wind speed, and solar radiation co-vary through time. Pearson correlation matrices and time-series alignment techniques were used to interpret cross-variable relationships, such as how cloud amount influences solar radiation, how humidity interacts with temperature, or how pressure variability corresponds to seasonal atmospheric transitions. The integration of these diagnostics provides a deeper understanding of the coupled nature of climate processes in the municipality and establishes a foundation for interpreting projected mid-century changes. 2.5 Future Climate Projection Framework Future climate trajectories for 2025–2050 were derived from the downscaled CMIP6 SSP245 dataset, which incorporates the physical principles of global climate models while providing local-scale climatic relevance through statistical downscaling. After harmonization with observed records, the projected monthly values for temperature, precipitation, humidity, solar radiation, and other variables were appended to the historical dataset to form a continuous time series extending to 2050. The projected data were analyzed using the same diagnostic tools applied to historical observations, allowing direct comparison of past and future climatic conditions. This approach retains the physical credibility of GCM-based projections while allowing them to be interpreted in the context of observed climatic variability and long-term regional trends. 3. Result 3.1 Temperature Variability in Chhedagad Municipality (1984–2024) The annual mean temperature series for all thirteen wards (Fig. 2 ) exhibit a clear interannual fluctuation superimposed on a gradual warming tendency across Chhedagad Municipality. Wards 1, 12, and 13 represent the warmest zones, with annual means consistently exceeding ~ 20°C, whereas the remaining wards cluster between ~ 12–14°C, indicating strong elevation-controlled thermal gradients within the municipality. Despite year-to-year variability, all wards display a similar temporal structure: warmer phases around the mid-1990s and early 2000s, followed by a short-term cooling dip around 2010–2015, and a renewed warming trend approaching 2020–2023. This synchronicity across wards suggests that regional-scale climate forcing dominates over local microscale influences Monthly climatological temperature profiles for all wards (Fig. 3 ) demonstrate a coherent seasonal cycle driven by the South Asian monsoon system. Temperatures begin increasing rapidly from March, peak during May-June, and decline steadily afterward, reaching their minima during December-January. The warmest wards (1, 12, 13) show peak monthly temperatures above 26°C, whereas the remaining wards peak near 17–19°C. Despite differences in magnitude, the seasonal shape remains consistent across wards, emphasizing spatial uniformity in seasonal heating and cooling patterns. Notably, the shoulder seasons (March-April and October-November) are transitional and reflect moderate thermal conditions, highlighting periods of climatic sensitivity relevant for agriculture and water-resource planning. Monthly climatology demonstrates a coherent seasonal cycle in all wards, with temperatures rising steadily from January to a pre-monsoon peak in May or June and declining toward December. Lower-elevation wards reach peak temperatures of about 26–28°C, while mid-elevation wards peak between 16°C and 20°C. These seasonal and long-term patterns together indicate a consistent warming signal across the municipality, forming an essential baseline for evaluating future temperature changes under CMIP6 SSP245 projections. 3.2 Precipitation Variability in Chhedagad Municipality (1984–2024) Annual precipitation patterns across the 13 wards of Chhedagad Municipality exhibit a clear upward trajectory over the 1984–2024 period when expressed as annual mean precipitation in mm/day (Fig. 4 ). In the early decades, most wards recorded annual mean values between 1.5–2.2 mm/day, representing a comparatively drier hydroclimatic baseline. Beginning around the early 2000s, however, nearly all wards show a substantial rise in mean precipitation, with values frequently reaching 3.0-4.5 mm/day in recent years. This increase is consistent across wards, indicating a municipality-wide intensification of rainfall rather than isolated variability. The post-2000 period also displays enhanced year-to-year fluctuations, suggesting increased hydrological instability and more variable monsoon performance. Monthly precipitation climatology further confirms the strong monsoonal influence on the region (Fig. 5 ). Across all wards, precipitation remains minimal during the winter and pre-monsoon months (January-April), with monthly mean values typically below 1 mm/day. A rapid escalation begins in May, culminating in a pronounced peak during July and August, where mean precipitation exceeds 8–10 mm/day depending on the ward. This narrow monsoon window contributes the majority of annual rainfall, highlighting the municipality’s dependence on a short yet intense precipitation season. Following the peak, rainfall declines sharply from September onward, reaching dry-season levels again by November-December. The uniformity of the monthly cycle across wards indicates that large-scale atmospheric dynamics, rather than local variability, dominantly control seasonal rainfall distribution. 3.3. Solar Radiation and Solar Energy Potential The spatiotemporal analysis of solar radiation and solar energy potential across all 13 wards of Chhedagad Municipality reveals coherent multi-decadal variability and a strong seasonal cycle, as shown in Fig. 6 . Annual solar radiation fluctuates between approximately 4.5 and 5.2 kWh/m²/day from 1984 to 2024, with most wards exhibiting higher radiation levels during the late 1980s through early 2000s. Following 2005, nearly all wards display a declining tendency, with several dropping toward 4.5 kWh/m²/day, marking some of the lowest values of the study period. A modest recovery is visible after 2020; however, radiation levels remain below earlier peak ranges, indicating a persistent long-term reduction in available solar irradiance. The monthly climatology in Fig. 6 further confirms a consistent seasonal radiation pattern across all wards. Solar radiation peaks during March-May, reaching 6.2–6.5 kWh/m²/day, representing the highest energy influx to the surface. The lowest values occur in December–January, typically declining to 3.5-4.0 kWh/m²/day, showing more than a 40% reduction compared to pre-monsoon maxima. Radiation decreases sharply during the monsoon season (June–September), stabilizing around 4.5-5.0 kWh/m²/day, reflecting the influence of cloud cover and atmospheric moisture. The similarity of seasonal curves across all wards confirms that the municipality experiences a uniform solar regime driven predominantly by regional atmospheric processes rather than localized microclimate variability. Solar energy potential, shown in Fig. 7 , displays annual totals ranging from approximately 1,650 to 1,850 kWh/m²/year, closely mirroring the temporal structure of solar radiation. Higher annual energy values dominate the period before 2005, with multiple wards consistently exceeding 1,800 kWh/m²/year. After 2005, most wards exhibit a downward trend, converging toward 1,650-1,700 kWh/m²/year during the mid-2010s. Although a slight improvement appears after 2020, these values remain lower than earlier maxima, indicating a sustained reduction in solar energy availability over recent decades. The monthly energy distribution in Fig. 7 emphasizes a distinct seasonal pattern consistent across all wards. Maximum monthly solar energy occurs during April-May, typically reaching 180–200 kWh/m², while the minimum occurs in December-January, around 110–130 kWh/m². Energy availability declines notably during the monsoon season, averaging 130–150 kWh/m², corresponding to reduced radiation from cloud cover and atmospheric scattering. This seasonal structure suggests that pre-monsoon months offer the highest efficiency for solar-based power generation, while winter months remain the least productive. Together, these results demonstrate that Chhedagad Municipality experiences (i) a measurable long-term decline in both solar radiation and solar energy potential after the early 2000s, (ii) a strong and consistent seasonal cycle across all wards, and (iii) a high degree of spatial uniformity in solar behavior across the municipality. Despite recent declines, the annual energy potential of ~ 1,650-1,850 kWh/m²/year confirms that the region retains favorable conditions for solar energy development, particularly during the pre-monsoon period when resource availability is at its peak. 3.4 Humidity Characteristics (Specific and Relative Humidity) The analysis of humidity patterns across all 13 wards of Chhedagad Municipality (Fig. 8 ) reveals distinct long-term variability and a robust seasonal structure in both specific humidity (g/kg) and relative humidity (%). Although specific humidity values remain comparatively low throughout the study period, both parameters exhibit coherent temporal behavior driven by temperature cycles and moisture availability. Annual specific humidity remains consistently between 2.5 and 6.0 g/kg across all wards, showing a gradual increasing tendency toward recent decades. Most wards exhibit a modest rise from the 1980s through the 2000s, followed by slight interannual fluctuations without any major decline. This upward drift aligns with the observed warming trend in the region (discussed earlier), as higher air temperatures enhance moisture-holding capacity. Despite the relatively small magnitude of specific humidity, its year-to-year variability is smooth and synchronous across the municipality, indicating that large-scale atmospheric moisture patterns, rather than local microclimatic differences, dominate humidity processes. Relative humidity shows more pronounced variability, with annual values ranging between 45% and 70% across wards. In nearly all wards, relative humidity exhibits a clear increasing pattern, particularly after the late 1990s, gradually rising toward 60–70% in the most recent decade. This long-term shift is visible consistently across the municipality, suggesting increasing atmospheric moisture presence or reduced evaporative demand in recent years. Interannual fluctuations are common, with minor dips corresponding to warmer and drier years, but the overall trajectory is upward across the entire 40-year period. Monthly climatology further clarifies humidity seasonality. Across all wards, specific humidity reaches its maximum during June, August, typically rising to 5–6 g/kg, driven by monsoonal moisture influx. Minimum values occur during December-January, where specific humidity frequently drops to 2.5-3.0 g/kg, coinciding with the cold and dry winter season. Relative humidity shows a similar seasonal pattern but with stronger amplitude: values peak during the monsoon (70–80%) and decline to 45–55% during the winter months. This consistent structure across all 13 wards reinforces the dominant influence of regional monsoon circulation in shaping humidity conditions. Overall, the combined annual and monthly analyses demonstrate that Chhedagad Municipality has experienced: (i) a steady long-term increase in both relative and specific humidity, (ii) strong seasonal humidity cycles governed by monsoon rainfall and winter dryness, and (iii) high spatial uniformity across all wards, confirming that humidity dynamics are primarily controlled by large-scale climatic processes rather than local variability. These findings provide a robust baseline for interpreting moisture-related climate impacts, hydrological responses, and future projections within the municipality. 3.5 Wind Speed Variability The long-term and seasonal wind speed characteristics across the 13 wards of Chhedagad Municipality (Fig. 9 ) exhibit clear temporal variability and a strong annual cycle consistent throughout the study period. Annual mean wind speeds generally range between 2.1 and 2.6 m/s, with substantial interannual fluctuations but no evidence of a persistent long-term increase. Instead, most wards display a gradual declining tendency after the early 2000s, where peak wind speeds often exceeded 2.5 m/s, followed by values frequently dropping toward 2.2–2.3 m/s in the last decade. This downward shift is consistently visible across multiple wards, indicating a municipality-wide reduction in average wind intensity during recent years. Interannual variability is pronounced in several wards, with short-term increases occasionally interrupting the broader declining pattern. For example, mid-1990s to early-2000s peaks approach or surpass 2.5 m/s in nearly all wards, while the period after 2010 shows fewer years exceeding 2.3–2.4 m/s, suggesting a moderation of wind activity. Despite these fluctuations, spatial uniformity is strong, as all 13 wards exhibit similar annual wind speed structures, implying control by regional atmospheric dynamics rather than localized topographic influences. Monthly climatology in Fig. 9 confirms a well-defined seasonal cycle. Wind speeds peak during March-May, where values rise to 2.6-3.0 m/s, representing the period of maximum air movement preceding the monsoon. The lowest wind speeds occur during October–December, commonly falling to 1.9–2.1 m/s, marking a reduction of nearly 30–40% from pre-monsoon maxima. During the monsoon months (June-August), wind speeds decline moderately but remain higher than winter levels, indicating sustained but stabilized atmospheric flow during this season. This seasonal pattern is consistent across all wards, demonstrating strong coherence in wind behavior throughout the municipality. Overall, the wind speed dataset reveals three key findings: (i) a moderate long-term decline in annual mean wind speed since the early 2000s, with most wards shifting from historical averages near 2.5 m/s toward 2.2–2.3 m/s in recent years, (ii) a robust and uniform seasonal cycle, characterized by strong pre-monsoon winds, moderated monsoon winds, and minimal post-monsoon and winter wind activity and, (iii) high spatial consistency across all 13 wards, indicating dominant control by regional-scale atmospheric circulation rather than local terrain effects. These results provide essential insights into changing wind dynamics within the municipality and offer a quantitative basis for evaluating wind-dependent environmental processes and renewable energy planning. 3.6 Cloud Amount Variability The annual and monthly cloud amount distribution across all 13 wards of Chhedagad Municipality (Fig. 10 ) demonstrates distinct long-term fluctuations and a pronounced seasonal cycle. Annual cloud amount generally varies between 40% and 60%, with consistent spatial agreement across all wards. Several wards show elevated cloudiness between the late 1990s and early 2010s, where annual values frequently rise to 55–60%, indicating a period of persistently higher atmospheric moisture and cloud cover. After 2015, most wards exhibit a declining tendency, with values commonly returning to the 45–50% range. Despite this decline, interannual variability remains evident, reflecting the influence of regional circulation patterns and monsoon dynamics. Monthly cloud climatology reveals a strong seasonal structure, with cloud cover peaking during the monsoon months (June-August) when values reach 75–85%, constituting the cloudiest period of the year across all wards. This pronounced peak reflects deep convective activity and sustained moisture influx during the South Asian monsoon. In contrast, the lowest cloud amounts occur in the post-monsoon and winter months (November-January), when values decrease to 35–45%, indicating clearer skies, reduced convection, and drier atmospheric conditions. The transition periods, pre-monsoon (March-May) and early post-monsoon (September-October), show moderate cloud levels, typically between 45% and 60%. The spatial consistency of cloud amount across all wards indicates that cloud formation in the municipality is predominantly driven by large-scale synoptic and seasonal climatic processes rather than local topographic variation. Annual cloud behavior closely aligns with observed trends in humidity and solar radiation, where years with higher cloud amount correspond to reduced radiation availability. The sustained monsoon peak and winter minimum observed in all wards demonstrate a uniform atmospheric response to regional seasonal forcing. Overall, cloud amount in Chhedagad Municipality is characterized by (i) substantial year-to-year fluctuations, (ii) a well-defined monsoon-dominated seasonal cycle, and (iii) high spatial coherence across wards. These factors play a critical role in modulating solar radiation, temperature variability, and local hydrometeorological processes. 3.7 Multivariate Correlation Structure of Climatic Variables The correlation matrices for all 13 wards of Chhedagad Municipality (Fig. 11 ) reveal a highly consistent multivariate structure, highlighting strong physical linkages among temperature, precipitation, solar radiation, cloud amount, and humidity variables. Despite geographic differences among wards, the correlation patterns demonstrate remarkable spatial uniformity, indicating that regional atmospheric processes drive climatic interactions across the municipality. A dominant relationship is the strong negative correlation between solar radiation and cloud amount, with coefficients typically ranging from − 0.92 to -0.97, representing one of the strongest associations in the dataset. This inverse relationship reflects the fundamental radiative effect of clouds, where increased cloud cover significantly reduces incoming solar radiation. This consistent and high-magnitude correlation across all wards verifies the strong regulatory role of cloudiness in modulating the surface energy budget. Temperature shows a moderate to strong negative correlation with precipitation (approximately − 0.35 to -0.45), indicating that wetter years and seasons tend to be cooler across the municipality. Conversely, temperature shows a positive correlation with solar radiation (around 0.30 to 0.45), demonstrating that clearer, less cloudy conditions are associated with higher surface heating. These relationships align with established climatological behavior and further validate the physical coherence of the dataset. Specific humidity (SHUM) exhibits one of the strongest internal consistencies across wards, showing a near-perfect positive correlation with relative humidity (RHUM) (typically 0.96–0.98). This reflects their shared dependence on atmospheric moisture content. SHUM also displays a moderate positive correlation with precipitation (~ 0.32–0.45), indicating that higher atmospheric moisture is linked to wetter conditions. At the same time, SHUM correlates negatively with solar radiation (approximately − 0.35 to -0.45), reinforcing the role of cloudy, moisture-rich environments in reducing solar flux. Relative humidity presents a correlation pattern similar to specific humidity but with slightly stronger negative relationships with temperature (≈ -0.48 to -0.55). This behavior reflects the thermodynamic relationship whereby warmer conditions decrease relative moisture saturation unless compensated by increased water vapor. Likewise, RHUM maintains a moderate positive correlation with precipitation (≈ 0.33–0.46), supporting its sensitivity to rainfall events and moist air masses. Cloud amount also demonstrates consistent correlations across all wards. In addition to its strong negative relationship with solar radiation, cloud amount shows a positive correlation with precipitation (~ 0.40–0.52) and moderate negative correlation with temperature (~ -0.35 to -0.45). These relationships confirm that cloudy years tend to be wetter and cooler, a hallmark of monsoon-driven climates. Overall, the correlation results exhibit four major scientifically supported outcomes (i) cloud amount is the primary regulator of surface solar energy, showing the strongest negative correlations across all wards., (ii) temperature decreases under wetter, cloudier, and moisture-rich conditions, demonstrating coherent thermodynamic behavior, (iii) specific humidity and relative humidity form a tightly linked moisture pair, showing near-perfect correlation throughout the municipality, and (iv) the correlation structure is spatially uniform, implying that climatic drivers affect all wards similarly, with minimal local deviation. These robust and physically consistent relationships provide a strong statistical foundation for interpreting climatic interactions in Chhedagad Municipality and support subsequent modeling and diagnostic analyses. 3.8 Projected Temperature Dynamics (2025–2050) The CMIP6 SSP2-4.5 driven projections indicate that Chhedagad Municipality is expected to experience a persistently warm thermal regime throughout 2025–2050, with notable interannual variability (Fig. 12 a). The annual mean temperature fluctuates between ~ 12.5°C and 14.5°C, showing several alternating warm and cool years rather than a strictly linear trend. Peaks around 2034, 2041, and 2047 suggest episodes of intensified warming, while dips around 2030, 2037, and 2043 indicate short-term cooling anomalies. Despite this variability, the overall trajectory suggests a gradually warming climate, with the latter half of the projection period generally warmer than the early 2030s. These fluctuations likely reflect the interaction of large-scale climate dynamics superimposed on a steadily warming baseline under SSP245 conditions The projected monthly climatology (Fig. 12 b) reveals a well-defined seasonal temperature cycle, mirroring the historical monsoon-driven thermal pattern but at elevated values. Winter months (December-February) remain the coolest of the year, ranging between 5–7°C, while spring months show a rapid warming transition, with temperatures rising to 11°C in March and surpassing 15°C by May. The warmest period is projected for June-August, with mean temperatures peaking at approximately 19-19.5°C in June. Following this summer peak, temperatures gradually decline through September and fall sharply after October, reaching ~ 7°C by December. This seasonal structure indicates that future warming is strongest in the pre-monsoon and monsoon months, which aligns with typical regional patterns of enhanced heat accumulation and reduced evaporative cooling under climate change scenarios. Together, these annual and seasonal projections highlight a future climate characterized by higher mean temperatures, amplified seasonal contrasts, and notable interannual variability. Such warming trends have important implications for local hydrology, agriculture, and heat-related hazards in Chhedagad Municipality. 3.9 Projected Precipitation Dynamics (2025–2050) The CMIP6 SSP2-4.5 precipitation projections indicate substantial interannual variability in future rainfall across Chhedagad Municipality, with no clear monotonic trend but evident fluctuations around a moderately increasing baseline (Fig. 13 a). Annual mean precipitation ranges approximately between 2.6 and 5.3 mm/day, reflecting alternating wet and dry years throughout the projection period. Several years, including 2034, 2040, 2042, and 2047, exhibit relatively elevated precipitation, while years such as 2030, 2036, and 2044 show noticeable reductions. This pattern suggests that while the overall rainfall regime remains variable, episodic high-rainfall years may become more frequent under mid-century climate conditions. The projected monthly climatology (Fig. 13 b) reveals a strongly seasonal precipitation cycle that mirrors the monsoon-dominated historical pattern but at slightly elevated values. Precipitation remains minimal during the winter and pre-monsoon period (January-April), generally below 1 mm/day. A rapid increase begins in May, culminating in a distinct monsoon peak between July and August, where mean precipitation rises to approximately 15–18 mm/day, representing the wettest period of the year. Following the monsoon peak, rainfall declines sharply after September, returning to dry-season conditions by November-December, with monthly means near or below 1 mm/day. This seasonal distribution highlights the continued dominance of the summer monsoon in shaping the municipality’s hydrological regime, with the mid-century climate maintaining a pronounced seasonal contrast. Together, the annual and monthly projections point to a future characterized by marked year-to-year variability and intensified monsoon season rainfall, reinforcing the need for adaptive water-resource planning, flood preparedness, and climate-resilient agricultural strategies in Chhedagad Municipality. 3.10 Projected Specific and Relative Humidity (2025–2050) The CMIP6 SSP2-4.5 projections indicate a coherent increase in atmospheric moisture content over Chhedagad Municipality during 2025–2050 (Fig. 14 a). Annual specific humidity fluctuates between 4–7 g/kg, showing recurrent humid anomalies in the early 2030s and late 2040s. Relative humidity varies between 55–70%, with modest interannual oscillations that parallel specific-humidity fluctuations. Although neither variable displays a strictly monotonic trend, the sustained higher-moisture years during mid-century suggest an enhanced capacity of the atmosphere to store water vapor. This behavior is consistent with thermodynamic expectations under warming scenarios, where increased temperature supports greater moisture-holding capacity. The monthly climatology (Fig. 14 b) reveals a seasonally stratified humidity regime. Specific humidity exhibits a clear minimum during December-February (≈ 3–4 g/kg), rises sharply with pre-monsoon warming, and peaks at 7–8 g/kg during July-August, coinciding with pronounced monsoonal moisture influx. Relative humidity follows a similarly phased pattern, with maxima during the monsoon core and suppressed values during the late winter and pre-monsoon months. Together, these projections indicate that mid-century atmospheric moisture dynamics will continue to be dominated by monsoon-driven processes, with implications for convective rainfall potential and local evapotranspiration fluxes. 3.11 Projected Solar Radiation (2025–2050) Projected all-sky surface shortwave radiation (Fig. 15 a) demonstrates moderate interannual variability, with annual means spanning 4.8–6.2 kWh/m²/day across the projection period. Years such as 2034, 2040, and 2047 show enhanced radiative loading, whereas 2030 and 2037 show comparatively reduced surface radiation, indicating periods of increased atmospheric cloudiness or aerosol optical effects. The absence of a persistent upward or downward long-term trajectory suggests that the CMIP6 SSP245 scenario maintains relatively stable radiative atmospheric conditions over mid-century. Monthly mean solar radiation (Fig. 15 b) preserves the strong seasonal cycle characteristic of Himalayan foothill climates. Radiation is lowest in December–January (≈ 3–4 kWh/m²/day), rises abruptly through the pre-monsoon months to peak in April-May at 7–8 kWh/m²/day, and then declines during the monsoon season due to high cloud cover. This seasonal asymmetry underscores the persistence of strong pre-monsoon solar forcing, which remains a critical window for energy harvesting and land-surface heating processes. 3.12 Projected Solar Energy Potential (2025–2050) Conversion of daily shortwave radiative fluxes into monthly solar-energy potential highlights significant seasonal and interannual variability (Fig. 16 a). Annual solar energy potential ranges between 140–170 kWh/m²/month, with high-yield years clustering around the mid-2030s and late 2040s. Such multi-year fluctuations carry practical significance for long-term off-grid solar system performance, particularly in remote municipalities where energy supply security is sensitive to climatic variability. The monthly projection (Fig. 16 b) shows a clear concentration of solar-energy availability in the pre-monsoon months. April and May consistently exceed 200 kWh/m²/month, representing the dominant contribution to annual solar energy. In contrast, July–August exhibit significant reductions due to monsoon cloud cover, and December–January represent the climatological minimum (< 110 kWh/m²/month). These patterns reinforce the need for seasonal load-management strategies and potential hybridization with wind or hydropower to ensure consistent year-round renewable energy supply. 3.13 Projected Wind Speed (2025–2050) Projected 10-m wind speeds (Fig. 17 a) reveal modest but meaningful interannual variability across the projection period. Annual mean wind speeds fluctuate between 2.8 and 4.0 m/s, with intermittent wind maxima in 2034, 2041, and 2047, and relative minima near 2030 and 2038. Such oscillatory behavior reflects the influence of regional synoptic patterns rather than long-term structural change in the wind regime under SSP245. Nonetheless, the maintenance of moderate wind speeds is favorable for small-scale hybrid renewable energy systems. The monthly climatology (Fig. 17 b) displays a distinct seasonal cycle. Wind speeds are weakest in December–January, strengthen rapidly during the pre-monsoon period, and peak in May-June at 4-4.5 m/s. During the monsoon, speeds moderate slightly due to increased atmospheric moisture and stability, before declining in the post-monsoon season. The persistence of strong pre-monsoon winds suggests potential opportunities for integrating wind-assisted energy generation during critical months of high demand and variable solar availability. 4. Discussion This study provides the first comprehensive multivariate climate assessment for Chhedagad Municipality, integrating four decades of satellite-derived observations (1984–2024) with CMIP6 SSP2-4.5 projections to 2050. The results reveal a spatially coherent climate system dominated by strong monsoonal forcing, elevation-controlled temperature gradients, and consistent inter-variable relationships that operate uniformly across all 13 wards. The diagnostic patterns observed in Chhedagad parallel broader climate trajectories documented for the Himalayan mid-hills, confirming that even remote municipalities experience regionally synchronized hydro-meteorological variability. Historical temperature records indicate a gradual warming tendency, particularly in lower-elevation wards (1, 12, and 13), which maintained annual averages exceeding ~ 20°C throughout the study period. The warm–cool oscillations, mid-1990s/early-2000s warm phases, and cooling dips around 2010–2015 resemble previously reported regional patterns associated with large-scale climate variability, including ENSO-modulated monsoon fluctuations. Despite local topographic complexity, the thermal regime remained highly coherent across wards, underscoring the dominance of synoptic processes over microscale terrain effects. Precipitation diagnostics reveal a distinct intensification of annual rainfall beginning in the early 2000s, accompanied by enhanced interannual variability, a hallmark of emerging monsoon instability noted across Nepal’s mid-western region. The monsoon peak (July-August) contributes the majority of annual rainfall, whereas the winter and pre-monsoon months remain markedly dry. This pronounced seasonality has significant implications for agriculture, slope stability, and water-security planning, particularly as rainfall extremes intensify under ongoing climate change. Humidity, cloud amount, wind speed, and solar radiation collectively demonstrate a physically consistent and tightly coupled climate system. Rising humidity levels, both specific and relative, align with warmer baseline temperatures and expanding atmospheric moisture capacity. Cloud amount shows clear monsoon-season dominance and is strongly inversely correlated with solar radiation (r ≈ -0.92 to -0.97), confirming its central role in regulating surface energy availability. Declining solar radiation and energy potential after the early 2000s appear linked to increased cloud cover and aerosol effects, consistent with regional dimming observed across South Asia. Meanwhile, wind speeds display a gradual weakening trend, reducing pre-monsoon maxima from > 2.5 m/s historically to ~ 2.2–2.3 m/s in recent years, which may reflect evolving regional circulation patterns. The multivariate correlation analyses reinforce the physical coherence of these findings. Temperature decreases during wetter, cloudier years; humidity strongly covaries with precipitation; and cloud amount governs both radiative fluxes and thermal responses. The near-identical correlation structure across all wards indicates shared atmospheric control rather than localized variability, highlighting the municipality’s climatic unity despite its rugged terrain. Future projections under SSP2-4.5 extend these patterns into mid-century. Temperatures are expected to rise gradually, particularly during pre-monsoon and monsoon months, increasing heat exposure and evapotranspirative demand. Precipitation remains highly variable, with episodic wet years possibly becoming more frequent—mirroring global model expectations for a more moisture-laden monsoon regime. Humidity projections suggest persistently moist atmospheric conditions, which may exacerbate heat stress and enhance convective potential. Solar radiation and energy potential remain seasonally reliable but fluctuating, with pre-monsoon months offering the highest renewable-energy potential. Wind projections exhibit modest interannual variability but retain a clear seasonal structure, with implications for hybrid energy planning. Overall, the study reveals a climate system characterized by increasing warmth, intensified monsoon rainfall, rising atmospheric moisture, reduced solar fluxes compared to earlier decades, and weakening winds, patterns consistent with regional Himalayan climate transformation. These findings underscore the need for localized, evidence-based adaptation strategies in agriculture, water management, and renewable energy design. 5. Conclusion This study provides the first comprehensive multivariate climate assessment for Chhedagad Municipality, revealing a climate system that is warming, increasingly moisture-laden, and marked by intensifying monsoon rainfall and rising hydro-climatic variability. The integration of four decades of NASA POWER observations with CMIP6 SSP2-4.5 projections demonstrates that temperature has gradually increased across all 13 wards, precipitation has strengthened and become more irregular after the early 2000s, and atmospheric moisture, expressed through both specific and relative humidity, has risen in parallel with these changes. Declining solar radiation and weakening wind speeds since the mid-2000s indicate broader shifts in regional atmospheric processes, while the strong and spatially uniform correlations among cloud amount, radiation, humidity, and temperature confirm that large-scale monsoon dynamics dominate the municipality’s climate behavior. Future projections to 2050 suggest continued warming, enhanced monsoonal moisture influx, and sustained interannual variability across temperature, rainfall, humidity, solar energy, and wind regimes. Together, these findings highlight a transition toward a warmer, wetter, and more variable mid-century climate, underscoring the need for evidence-based adaptation strategies in agriculture, water-resource management, energy planning, and hazard preparedness. By establishing a robust historical baseline and a reliable future trajectory, this study fills a critical knowledge gap for Chhedagad and provides essential scientific guidance for local climate-resilient development. References Abdullah T, Bashir J, Romshoo SA (2025) Assessing glacier changes and hydrological impacts in the upper Indus Basin under CMIP6 climate scenarios. iScience 28(8):113200. https://doi.org/10.1016/j.isci.2025.113200 Bastola S, Cho J, Kam J, Jung Y (2024) Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models. Atmos Res 311:107720. https://doi.org/10.1016/j.atmosres.2024.107720 Chhedagar Municipality (2025), Karkigaun, Jajarkot, Karnali Province, Nepal. | Prosperous Chhedagar, Our Future. (n.d.). Retrieved November 29, from https://chhedagadmun.gov.np/ Colston JM, Ahmed T, Mahopo C, Kang G, Kosek M, De Sousa Junior F, Shrestha PS, Svensen E, Turab A, Zaitchik B (2018) Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study. Environ Res 165:91–109. https://doi.org/10.1016/j.envres.2018.02.027 Dahal P, Shrestha ML, Panthi J, Pradhananga D (2020) Modeling the future impacts of climate change on water availability in the Karnali River Basin of Nepal Himalaya. Environ Res 185:109430. https://doi.org/10.1016/j.envres.2020.109430 Dhakal S, Gautam Y, Bhattarai A (2020) Evaluation of Temperature-Based Empirical Models and Machine Learning Techniques to Estimate Daily Global Solar Radiation at Biratnagar Airport, Nepal. Adv Meteorol 2020(1):8895311. https://doi.org/10.1155/2020/8895311 Jajarkot topographic map, elevation, terrain . (n.d.). Topographic Maps. Retrieved November 29, (2025) from https://en-gb.topographic-map.com/map-z1xptj/Jajarkot/ Jha AN, Bhattarai U, Lamichhane M, Mehan S, Ghimire S, Bhattarai PK (2025) Assessing hydropower production potential with multisite hydrological modeling in the Koshi river Basin, Nepal. Energy Convers Management: X 28:101283. https://doi.org/10.1016/j.ecmx.2025.101283 Joshi M, Dhital H (2023) Impacts of Climate Change on Temperature and Precipitation in Nepal: Projections and Bias Correction. J Sustain Environ Manage 2:203–212 Karimzadeh S, Ahmadi A, Baldocchi D, Fisher JB (2025) Climate change has increased global evaporative demand except in South Asia. Commun Earth Environ. https://doi.org/10.1038/s43247-025-02959-x Lamichhane M, Phuyal S, Mahato R, Shrestha A, Pudasaini U, Lama SD, Chapagain AR, Mehan S, Neupane D, Lamichhane M, Phuyal S, Mahato R, Shrestha A, Pudasaini U, Lama SD, Chapagain AR, Mehan S, Neupane D (2024) Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool. Sustainability 16(8). https://doi.org/10.3390/su16083262 Li Y, Wang C, Su F (2021) Evaluation of CMIP6 Models over Two Third Pole Subregions with Contrasting Circulation Systems . https://doi.org/10.1175/JCLI-D-21-0214.1 NASA POWER | Data Access Viewer (DAV) . (n.d.). Retrieved December 7, (2025) from https://power.larc.nasa.gov/data-access-viewer/ Population | National Population and and Housing Census 2021 Results . (n.d.). Retrieved November 29, (2025) from http://censusnepal.cbs.gov.np/results/population?province=6&district=65&municipality=4 Pradhananga S, Nepal S, Kamal SK, Hafeez M (2025) Climate change will exacerbate seasonal flow variability in the Karnali River basin: Implications for water, energy, and agriculture sectors. Hydrol Res 56(6):471–491. https://doi.org/10.2166/nh.2025.193 Rawat G (2025) Four-decade hydro-meteorological variability and machine-learning based weather projections (1983–2050) for Nalphu village, Jajarkot, Nepal using XGboost. Res Square. https://doi.org/10.21203/rs.3.rs-8262924/v1 Rawat G, Khanal N, Adhikari PB (2025) Characterization and Morphological Analysis of Trio-Glacial Lakes (Sankha Lake, Kukur Lake, and Thakur Jyu Lake) of Barekot Patan, Jajarkot, Nepal. Contemp Research: Interdisciplinary Acad J 8(2):100–116. https://doi.org/10.3126/craiaj.v8i2.86413 Saha A, Jain MK, Joshi P, Das S, Rawat NS (2025) Development of daily downscaled, bias-corrected CMIP6 climate datasets for estimating reference evapotranspiration (ETo) in South Asia. Sci Data 12(1):1879. https://doi.org/10.1038/s41597-025-06149-4 Shrestha E, Poudyal S, Ghimire A, Maharjan S, Lamichhane M, Mehan S (2025) Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal. Results Eng 25:104254. https://doi.org/10.1016/j.rineng.2025.104254 Sigdel KP, Ghimire NP, Pandeya B, Dawadi B, Sigdel KP, Ghimire NP, Pandeya B, Dawadi B (2022) Historical and Projected Variations of Precipitation and Temperature and Their Extremes in Relation to Climatic Indices over the Gandaki River Basin, Central Himalaya. Atmosphere 13(11). https://doi.org/10.3390/atmos13111866 Subedi SR, Lamichhane M, Dhungana S, Chalise B, Bhattarai S, Chaulagain U, Khatiwada R (2024) Assessing the impact of climate change on streamflow in the Tamor River Basin, Nepal: An analysis using SWAT and CMIP6 scenarios. Discover Civil Eng 1(1):135. https://doi.org/10.1007/s44290-024-00143-2 Ullah S, You Q, Zhang Y, Bhatti AS, Ullah W, Hagan DFT, Ali A, Ali G, Jan MA, Khan SN, Ali A (2020) Evaluation of CMIP5 models and projected changes in temperatures over South Asia under global warming of 1.5 oC, 2 oC, and 3 oC. Atmos Res 246:105122. https://doi.org/10.1016/j.atmosres.2020.105122 Yang X, Shen C, Ullah I, Curio J, Chen D (2024) Evaluating heat stress and occupational risks in the Southern Himalayas under current and future climates. Npj Clim Atmospheric Sci 7(1):211. https://doi.org/10.1038/s41612-024-00764-5 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8570798","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572629869,"identity":"b2ee0fec-c628-4c88-93a5-34075128880c","order_by":0,"name":"Ganesh Rawat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACxgaGBAaGAwwMbCDeByBmYydSiwRIC+MMkBZmouwCagFRzDxgkoBi5vYDDz9XnLGp4xM7/Oyxza9t8nzMDIwfPubgcVhPQrLkmRtpEmzSaebGuX23DduYGZglZ27D55eEBMmGD4eBWhLMpHN7bjMCtbAx8+LT0v8g+SdES/o3acue2/aEtcxISJNsuAHSkmMmzfDjdiIRWh6kWTacSZNsk84pk+xtuJ3cxszYjNcvhv05yTcbjtnwy89O3ybx489t2/ntzQc/fMSnpYEnAcnONjDZgFs9EMgzsB9A4v7Bq3gUjIJRMApGKAAAjdRQb627FasAAAAASUVORK5CYII=","orcid":"","institution":"Tri-Chandra Multiple Campus (Tribhuvan University)","correspondingAuthor":true,"prefix":"","firstName":"Ganesh","middleName":"","lastName":"Rawat","suffix":""}],"badges":[],"createdAt":"2026-01-11 00:18:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8570798/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8570798/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100366372,"identity":"fab02d26-94bd-49f5-b532-6dfcf8cac303","added_by":"auto","created_at":"2026-01-16 07:56:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4784993,"visible":true,"origin":"","legend":"","description":"","filename":"ChhedagadMultivariateClimateDiagnosticsandCMIP6SSP245.docx","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/c156223da78cb262d0d2a4be.docx"},{"id":100367334,"identity":"d6d98677-acda-48ab-89e7-2e413a48ba5b","added_by":"auto","created_at":"2026-01-16 07:56:58","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8570798.json","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/8f74f7ce2e9d18096d95d31b.json"},{"id":100366913,"identity":"ef7dd29e-e60f-4900-b5fa-f98ffe3453a4","added_by":"auto","created_at":"2026-01-16 07:56:39","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115180,"visible":true,"origin":"","legend":"","description":"","filename":"rs85707980enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/a704bcd0951e0264495d751c.xml"},{"id":100366370,"identity":"c8bfa213-d83f-4034-ad20-e23becfc39a8","added_by":"auto","created_at":"2026-01-16 07:56:17","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1012113,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/7d7be693fd0712ed75dbb691.png"},{"id":100129951,"identity":"54657d86-e489-4733-a249-c0e7d1f5d17a","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":559017,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/10cbf837e6eb57c37e4249db.png"},{"id":100129967,"identity":"995423df-2ab9-45a0-bb86-31f4a50ac6bf","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":139820,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/cf7f9b73fbcb07083801ebad.png"},{"id":100367055,"identity":"598ee989-772e-4c3f-9d5f-9768b530ce69","added_by":"auto","created_at":"2026-01-16 07:56:45","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79898,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/81d3fba762dd211ad16f528b.png"},{"id":100366554,"identity":"8d2f8c40-52f5-4b8a-bbee-da726149e55a","added_by":"auto","created_at":"2026-01-16 07:56:20","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80797,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/141d9b8186ca57a86e1d1c3b.png"},{"id":100129960,"identity":"89b98c77-e168-4e11-b662-6ca6554a2d2f","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62474,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/244defbbc29273cbbf9d66a0.png"},{"id":100366478,"identity":"2a608a0c-db19-4f12-b35c-039a9fa2821d","added_by":"auto","created_at":"2026-01-16 07:56:19","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73565,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/b3fa6dbd7ebf47e5f5304288.png"},{"id":100129987,"identity":"9e16c639-305e-4e0c-b6dd-6cfd358933a3","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68474,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/a25a9ee1796b1e1f2c26486d.png"},{"id":100129955,"identity":"fa7ebecd-52c7-4845-b822-627bb5865b52","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68504,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/e5c8602ae191533a6ba84505.png"},{"id":100129972,"identity":"67d72f16-2ac5-47cf-914a-822208e685da","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":235754,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/b95c6c5edda19f97c628d5d4.png"},{"id":100365840,"identity":"2c2b7635-4913-4e09-81e5-54fe2af601b0","added_by":"auto","created_at":"2026-01-16 07:55:41","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165923,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/6075959e0acc0d15099ff39e.png"},{"id":100366742,"identity":"ff860d25-5fd5-4fb8-921a-31df5bf9fc38","added_by":"auto","created_at":"2026-01-16 07:56:31","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":230603,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/d562ad41c8418d27b5d22949.png"},{"id":100129966,"identity":"6373717f-aedb-4fe2-b999-57021ac43c3a","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122436,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/de23872f74a6f594cce21a34.png"},{"id":100129975,"identity":"e0772bee-879a-417c-8f7b-27c0dcd82136","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":463318,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/901c5bdf73148c5ddf98c75d.png"},{"id":100366975,"identity":"5d1093ff-6478-4eda-a99c-372328bda9e3","added_by":"auto","created_at":"2026-01-16 07:56:42","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":451159,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/661becf686d417e739c73bcc.png"},{"id":100366888,"identity":"1a79dd14-0918-436a-bd14-3c1d0096a1ee","added_by":"auto","created_at":"2026-01-16 07:56:37","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":433560,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/7114933dfdfebe548f211d84.png"},{"id":100366085,"identity":"4dbfead6-c076-4a7d-95fc-282669c2330e","added_by":"auto","created_at":"2026-01-16 07:55:56","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":459160,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/8c470d6a77f35188fdd70f77.png"},{"id":100367085,"identity":"7bdb8c05-a657-4a65-a1ef-dc5b5b8ca1d9","added_by":"auto","created_at":"2026-01-16 07:56:45","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136978,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/145e6cd5b4283067c4b66fa6.png"},{"id":100129983,"identity":"feb7797b-820b-463e-95d6-fd3b97a315b5","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174366,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/a0bf3564a46c9ea98a84de32.png"},{"id":100366246,"identity":"477cecd3-1b32-4efb-8adf-be65d6a53c75","added_by":"auto","created_at":"2026-01-16 07:56:09","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50503,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/f296ce9df9d3f947f95de002.png"},{"id":100129980,"identity":"c6b7da9d-2fda-40db-bc16-9f3c6853d760","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26730,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/a10f9a41238cc2ab420e8248.png"},{"id":100129976,"identity":"a2500019-28fa-490e-bdd8-de9412ea938b","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":27496,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/93f387c4065202cf33784628.png"},{"id":100367117,"identity":"74dbca97-978e-49e4-b5e7-708b6617edc1","added_by":"auto","created_at":"2026-01-16 07:56:47","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19132,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/9b2c2a191a7472f230dd7a0b.png"},{"id":100366173,"identity":"971aef30-3314-4bb6-835d-d7dd7a597a20","added_by":"auto","created_at":"2026-01-16 07:56:03","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22999,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/99e4cb39ca34e7fecd273288.png"},{"id":100366447,"identity":"ef84b8eb-1eb9-4d21-90a3-5cbfe5150195","added_by":"auto","created_at":"2026-01-16 07:56:19","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23243,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/6fd8fa284599a5333a45a633.png"},{"id":100129974,"identity":"f11c4886-832a-4820-a00e-7e95ecac2861","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24259,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/14cacda7a9d2089e14f0ccba.png"},{"id":100129982,"identity":"b47ec2b2-e308-47d7-a8cd-a5a6c5a9999b","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":100090,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/46b67e0017b67d11bdfd6096.png"},{"id":100366136,"identity":"c1521555-d4ff-418c-8cc8-5bd300830a74","added_by":"auto","created_at":"2026-01-16 07:56:00","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65859,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/ecae3defec5ac450c383351f.png"},{"id":100129978,"identity":"b3a9d751-57ea-4b5e-af06-76244883f0e9","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84543,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/129e9aa612278a5222cb1411.png"},{"id":100129994,"identity":"b3924419-149b-46a7-917d-7c70de8bfbed","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49371,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/2e40a633a4f58ed8e412385b.png"},{"id":100129991,"identity":"0040d298-3db4-43ab-86c9-8a6774a9cf87","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151969,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/d2c8d326c11d297a77c5c664.png"},{"id":100129984,"identity":"67cdf946-c020-4cf7-99de-1b1dfec1d599","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144338,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/c368e1530c7488f895a7ee11.png"},{"id":100367993,"identity":"f650ea64-4c79-4c94-9e52-97aba5d964f9","added_by":"auto","created_at":"2026-01-16 07:57:30","extension":"png","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122754,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/257c88accbc8e050c45afaee.png"},{"id":100367919,"identity":"df9a93ad-a1b7-425c-ada2-b3fa885e68a3","added_by":"auto","created_at":"2026-01-16 07:57:27","extension":"png","order_by":36,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":135041,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/6d9090f261ad897b9df3abd8.png"},{"id":100368002,"identity":"b4d4f66c-17d1-4ca1-97f8-f0b1e369a939","added_by":"auto","created_at":"2026-01-16 07:57:30","extension":"xml","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114391,"visible":true,"origin":"","legend":"","description":"","filename":"rs85707980structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/db86fb28d0dea5fe31b9f98e.xml"},{"id":100366761,"identity":"abfeda6d-9dee-49f2-9241-3fa2d65ef586","added_by":"auto","created_at":"2026-01-16 07:56:31","extension":"html","order_by":38,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122019,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/95169090e0e451a8d1be9ad1.html"},{"id":100129941,"identity":"942a3694-524a-45f9-8f1a-dc0eec0462d9","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1012113,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area (Chhedagad Municipality, Jajarkot, Nepal)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/25762089b7666da59581b5c4.png"},{"id":100129943,"identity":"7f8e9ab7-4184-442e-8fe9-8f1369adb94d","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235754,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual mean temperature trends (1984-2023) for all 13 wards, showing interannual variability and a gradual warming pattern across Chhedagad Municipality.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/102fba9171b8c034f60489a8.png"},{"id":100129940,"identity":"d50e73eb-ccb5-4c04-b89e-cbe87bb8d03f","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165923,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly temperature climatology for 13 wards, illustrating a uniform seasonal cycle with peak temperatures in May-June and minima in December-January.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/84c131a265b716ee123a8785.png"},{"id":100129962,"identity":"3cee6d36-868d-49a6-b7f3-f7a8726d5b01","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":230603,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual precipitation trends (1984-2023) across 13 wards, indicating increasing rainfall and higher variability in recent decades.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/69c830d09a4ca998cac28ef7.png"},{"id":100129942,"identity":"e4f449ac-9def-4572-90c9-7fbd762d48d6","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122436,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly precipitation climatology for 13 wards, showing a dominant monsoon peak in July-August and dry-season conditions from November to April.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/1288d3ca35c6294c78f45756.png"},{"id":100366336,"identity":"06bdd4f4-e90f-43be-a628-645d1d892993","added_by":"auto","created_at":"2026-01-16 07:56:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":463318,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and monthly solar radiation patterns for all 13 wards of Chhedagad Municipality (1984-2024), showing multi-decadal variability and a consistent seasonal peak during March-May.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/e0a0a32b0586e38b7e78da28.png"},{"id":100367268,"identity":"f5aed61d-6705-44e4-b085-da5be7dbb493","added_by":"auto","created_at":"2026-01-16 07:56:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":451159,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and monthly solar energy potential across the 13 wards, illustrating long-term variability and maximum energy availability during the pre-monsoon season.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/5aa72b0a5b67baf563db79ef.png"},{"id":100367681,"identity":"71533599-a284-4f0d-8441-79f818349bc9","added_by":"auto","created_at":"2026-01-16 07:57:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":433560,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and monthly patterns of specific humidity (g/kg) and relative humidity (%) across all 13 wards of Chhedagad Municipality (1984-2024), showing long-term increases and a consistent seasonal peak during the monsoon months.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/c8b98e34b311c6205d56669a.png"},{"id":100366589,"identity":"253d505a-89ae-4a08-af08-5b798a566f1e","added_by":"auto","created_at":"2026-01-16 07:56:22","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":459160,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and monthly wind speed patterns across all 13 wards of Chhedagad Municipality (1984-2024), showing a strong pre-monsoon peak and a gradual decline in annual wind speeds in recent decades.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/2bee8d471b338388c3cde632.png"},{"id":100129947,"identity":"a52bdbfa-9b87-4b5a-b4f7-b35d2a0e12e4","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":559017,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and monthly cloud amount across all 13 wards of Chhedagad Municipality (1984-2024), showing strong monsoon peaks (75-85%) and winter minima (35-45%), with notable interannual variability.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/240ae902869f36ef59f65259.png"},{"id":100366756,"identity":"06d21e24-56e6-4dfb-a423-dc13af88ec2a","added_by":"auto","created_at":"2026-01-16 07:56:31","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":139820,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation matrix of key climatic variables (temperature, precipitation, solar radiation, cloud amount, specific humidity, and relative humidity) across all 13 wards of Chhedagad Municipality, showing strong and consistent relationships, including the dominant negative correlation between solar radiation and cloud amount and the near-perfect coupling between specific and relative humidity.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/7a80fd32c25ecb837a9cc809.png"},{"id":100367426,"identity":"e252e132-9f50-4e9e-b8d9-5ff09dbc7281","added_by":"auto","created_at":"2026-01-16 07:57:03","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":79898,"visible":true,"origin":"","legend":"\u003cp\u003eProjected temperature for 2025-2050 under SSP2-4.5: (a) Annual mean temperature (b) Monthly mean temperature climatology.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/255d44827160233e639ce2d5.png"},{"id":100365785,"identity":"dc63a9a0-c7f1-4433-a539-84eb5fd6242b","added_by":"auto","created_at":"2026-01-16 07:55:38","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":80797,"visible":true,"origin":"","legend":"\u003cp\u003eProjected precipitation for 2025-2050 under SSP2-4.5: (a) Annual mean precipitation (mm/day), (b) Monthly mean precipitation climatology.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/6dc08eddc9ff54974c851674.png"},{"id":100367064,"identity":"d77addac-e0bd-4864-ac1d-949c97d7d2fe","added_by":"auto","created_at":"2026-01-16 07:56:45","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":62474,"visible":true,"origin":"","legend":"\u003cp\u003eHumidity projections for 2025-2050:\u003cstrong\u003e (a)\u003c/strong\u003e Annual specific and relative humidity, \u0026nbsp;\u003cstrong\u003e(b)\u003c/strong\u003eMonthly specific and relative humidity climatology.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/c3962b12beff1150cb47bda3.png"},{"id":100129968,"identity":"d94f7673-6a0c-46e2-a320-9ea88bc8e793","added_by":"auto","created_at":"2026-01-13 10:02:47","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":73565,"visible":true,"origin":"","legend":"\u003cp\u003eSolar radiation projections for 2025-2050:\u003cstrong\u003e(a)\u003c/strong\u003e Annual mean shortwave radiation, \u003cstrong\u003e(b)\u003c/strong\u003e Monthly solar radiation climatology.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/210cc9284d0dbd0b83b37abc.png"},{"id":100366880,"identity":"d0613864-6569-431b-9508-d258a55b82a9","added_by":"auto","created_at":"2026-01-16 07:56:36","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":68474,"visible":true,"origin":"","legend":"\u003cp\u003eSolar energy potential for 2025-2050: \u003cstrong\u003e(a)\u003c/strong\u003e Annual solar energy potential (kWh/m²), \u003cstrong\u003e(b)\u003c/strong\u003e Monthly solar energy potential.\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/0e924bb317c45f1f20ba1885.png"},{"id":100129970,"identity":"521c631a-6008-4022-b1cc-c6085eac738d","added_by":"auto","created_at":"2026-01-13 10:02:48","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":68504,"visible":true,"origin":"","legend":"\u003cp\u003eWind speed projections for 2025-2050: \u003cstrong\u003e(a)\u003c/strong\u003e Annual mean wind speed (m/s), \u003cstrong\u003e(b)\u003c/strong\u003e Monthly wind speed climatology.\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/a615a29a0ac2c5e8b38683a2.png"},{"id":100382305,"identity":"ec840bc3-fa95-473a-8e30-a3e6a1dc5410","added_by":"auto","created_at":"2026-01-16 10:42:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4912709,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8570798/v1/c1283aa5-d0e3-41c9-bada-54c33554d073.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMultivariate Climate Diagnostics and CMIP6 SSP245-Driven Future Pathways for Chhedagad Municipality, Jajarkot, Nepal (1984-2050)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRecent climate research across the Himalayan region consistently shows strong warming signals and increasing hydro-climatic variability, underscoring the need for localized climate diagnostics and high-resolution future projections. Studies such as (Sigdel et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrate significant historical increases in both precipitation and temperature across mountainous basins, with CMIP6 models projecting further intensification of monsoon rainfall and a pronounced rise in Tmin under intermediate and high-emission pathways. These findings align with broader regional assessments indicating rising warm extremes, declining winter precipitation, and increasing frequency of heavy rainfall events, factors that heighten risks of floods, landslides, droughts, and glacier-related hazards in Nepal\u0026rsquo;s mid- and far-western districts. Despite these advances, fine-scale multivariate analyses that integrate temperature, precipitation, cloud dynamics, humidity, wind behaviour, and solar-energy patterns remain limited for remote municipalities such as Chhedagad in Jajarkot. This gap highlights the importance of the present study, which uses observed climatology (1984\u0026ndash;2024) together with CMIP6 SSP2-4.5 projections to provide a comprehensive diagnostic of multi-parameter climate behaviour and future pathways relevant for agriculture, water resources, and climate-hazard preparedness in Chhedagad Municipality.\u003c/p\u003e \u003cp\u003eResearch on high-altitude climate and hydrological systems in Jajarkot has begun to highlight the strong influence of climate variability on local environmental processes. (Rawat et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed that the trio of glacial lakes in Barekot Patan is undergoing noticeable morphological changes driven by long-term glacier retreat, increased snowmelt, and shifting precipitation patterns, emphasizing rising GLOF-related risks in the region. Building on this regional understanding, (Rawat, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) further demonstrated that hydro-meteorological conditions in nearby Nalphu Village exhibit clear multi-decadal variability, including a statistically significant cooling trend after the early 2000s and a strong shift toward wetter monsoon conditions, with machine-learning forecasts indicating continued moisture-rich atmospheric behavior and moderate warming through 2050. Together, these studies highlight how climate-driven changes in temperature, precipitation, and cryospheric processes are reshaping both hydrological regimes and high-altitude landscapes of Jajarkot, underscoring the need for localized climate diagnostics and adaptation planning.\u003c/p\u003e \u003cp\u003eClimate research in Nepal shows consistent warming and shifting precipitation patterns, with rising extremes due to complex topography and limited observational coverage. Studies highlight that CMIP6 models provide improved climate projections, though bias correction, especially quantile mapping, is essential for reducing model uncertainty and producing reliable local estimates (Joshi \u0026amp; Dhital, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). similarly reports strong future increases in temperature and precipitation across Nepal, particularly under high-emission scenarios, reinforcing earlier evidence of heightened climate vulnerability in regions like Karnali and the central hills. Overall, existing literature agrees that Nepal is moving toward warmer and wetter conditions, underscoring the need for localized climate diagnostics and future pathway assessments.\u003c/p\u003e \u003cp\u003eRecent studies across Nepal and the Himalayan region consistently report significant warming and increasing precipitation variability, driven by the region\u0026rsquo;s strong elevation-dependent climatic response. CMIP6 models have shown improved capability in representing these patterns, especially in complex basins such as Karnali, where bias correction substantially enhances model realism. The uploaded study by (Lamichhane et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrates clear future increases in precipitation and temperature across the basin, alongside marked changes in streamflow and baseflow patterns, emphasizing rising climate-induced hydrological stress. Similar research highlights that minimum temperatures are rising faster than maximum temperatures, and monsoon intensification is increasingly evident across western Nepal. Collectively, the literature underscores Nepal\u0026rsquo;s high climate sensitivity and the need for localized diagnostics and future projections to support adaptation planning. Recent climate studies in Nepal show clear warming trends and increasing precipitation extremes, especially in high-altitude regions. CMIP6-based research provides improved future projections, but downscaling is critical due to sparse meteorological data. The uploaded study confirms strong increases in extreme rainfall and temperature indices across all SSP scenarios for Nepal (Bastola et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, existing literature agrees that Nepal is moving toward a warmer and wetter climate with more intense extremes, highlighting the need for localized climate assessments.\u003c/p\u003e \u003cp\u003eClimate studies across Nepal consistently show rising temperatures and increasing precipitation extremes, especially in mountain basins. Recent CMIP6-based research provides improved projections, highlighting significant warming and stronger monsoon intensification under higher-emission pathways. The uploaded Tamor Basin study also reports clear increases in temperature and precipitation across SSPs, reinforcing Nepal\u0026rsquo;s growing hydro-climatic vulnerability (Subedi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Together, the literature underscores an urgent need for localized climate diagnostics to support adaptation planning. According to (Dahal et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the study shows clear evidence of increasing temperature trends and shifting precipitation patterns across Nepal\u0026rsquo;s diverse climatic zones. Their analysis highlights a rising frequency of hydro-climatic extremes, driven by both seasonal variability and long-term warming signals. He further emphasize that these changes intensify local vulnerability, demonstrating the need for detailed, municipality-scale climate assessments such as the present research. (Ullah et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) evaluated 21 CMIP5 models and showed that South Asia is experiencing consistent warming, with Tmin, Tmax, and Tmean all increasing under 1.5\u0026deg;C, 2\u0026deg;C, and 3\u0026deg;C global warming thresholds. Their results indicate that warming intensifies toward higher latitudes, especially across the Hindu Kush\u0026ndash;Himalayan region, where temperature extremes are projected to strengthen rapidly. Ullah et al. further demonstrated that warming arrives earlier in South Asia than the global average, highlighting high regional vulnerability. This study provides strong evidence that future climate conditions will amplify thermal extremes and increase hydro-climatic risks across the region.\u003c/p\u003e \u003cp\u003e(Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) showed that CMIP6 models generally reproduce the spatial and seasonal climate patterns of the Third Pole but still retain substantial cold and wet biases, especially over monsoon-dominated regions. Their evaluation further demonstrated that future warming in these high-altitude areas is projected to exceed global averages, with stronger wetting trends in summer for monsoon-influenced zones. Li et al. also emphasized persistent model uncertainties due to complex topography and circulation interactions, underscoring the need for localized climate analyses such as the present study. (Saha et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) developed high-resolution, bias-corrected CMIP6 datasets to improve ETo estimation across South Asia, addressing major warm, radiation, and humidity biases found in raw model outputs. Their results show that quantile-mapping correction substantially enhances agreement with ERA5 observations, making the datasets suitable for local climate-impact analysis. Projected ETo increases, especially under higher-emission scenarios, indicate growing water-stress risks across regions such as Afghanistan and northern India. Overall, the study provides a robust foundation for reliable hydrological and agricultural planning under future climate change. Glacier change assessments in the Upper Indus Basin reveal rapid mass loss and substantial area reduction, as demonstrated by (Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) who show that warming under SSP245 and SSP585 will significantly accelerate glacier retreat. Their results indicate that even modest precipitation increases cannot offset the strong temperature-driven melt, leading to projected deglaciation exceeding 55% by the 2080s. According to him these changes pose serious implications for future water availability, hydropower, and agricultural demand in the region. Overall, the attached study provides clear evidence of severe cryospheric vulnerability under ongoing climate change.\u003c/p\u003e \u003cp\u003eRecent assessments show that the Southern Himalayas face some of the world\u0026rsquo;s most severe heat-stress risks, with WBGT levels frequently exceeding safe thresholds, as demonstrated by (Yang et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Their analysis reveals rapidly rising hours in Categories 3\u0026ndash;5, indicating intensifying impacts on outdoor labor and human health. The study further shows that under SSP2-4.5 and SSP5-8.5 scenarios, extreme heat events similar to 2020 will become common, exposing millions to dangerous heat stress. According to Yang et al., climate change, rather than atmospheric circulation, is the primary driver of this escalating risk. (Karimzadeh et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) rapid warming across the Tibetan Plateau is driving significant shifts in atmospheric heat sources, altering monsoon strength and regional moisture transport. Their analysis shows that heat-source intensity has strengthened in recent decades, leading to enhanced land-atmosphere coupling and greater climate sensitivity in downstream regions. He further note that these changes intensify both summer precipitation variability and extreme climate responses across Asia, highlighting the Plateau\u0026rsquo;s growing influence on broader monsoon dynamics. Severe heat events across South Asia have intensified over recent decades, with (Colston et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) showing that wet-bulb temperatures are approaching physiological limits under continued warming. Their analysis demonstrates that both dry-bulb and humidity-driven heat stress will sharply increase under high-emission scenarios, making previously rare extremes more frequent and more dangerous.\u003c/p\u003e \u003cp\u003e(Dhakal et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that empirical temperature-based equations and machine-learning models can effectively estimate evapotranspiration across Nepal when conventional FAO-56 inputs are limited, highlighting the strong sensitivity of ETo to local climatic variability and the advantages of data-driven techniques in mountainous environments. Building on this, (Shrestha et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) showed that advanced ML models such as XGBoost, DNN, and LSTM consistently outperform traditional empirical methods when predicting ETo across Nepal\u0026rsquo;s diverse agro-meteorological zones, with radiation-based inputs yielding the highest accuracy and station clustering further reducing prediction error.\u003c/p\u003e \u003cp\u003eRecent advancements in hydro-climatic modeling across Nepal highlight the growing reliance on data-driven and physically based approaches to understand climate variability and its sectoral impacts. Temperature-based empirical and machine-learning techniques have been shown by (Dhakal et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to greatly improve evapotranspiration estimation under limited-data conditions. At the basin scale, (Jha et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) used a multi-site SWAT framework with CMIP6 projections to show that future hydrology in the Koshi Basin will experience increased discharge and enhanced hydropower potential, particularly when adaptive design discharges are applied. Complementing this, (Pradhananga et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that the Karnali Basin will face pronounced seasonal shifts, with monsoon flows increasing and winter\u0026ndash;spring flows declining, which in turn reduces dry-season energy reliability and increases irrigation water demand. Collectively, these studies highlight the critical need for localized, high-resolution climate diagnostics and integrated modeling frameworks to support resilient water, energy, and agricultural planning under accelerating climate change.\u003c/p\u003e \u003cp\u003eChhedagad Municipality remains critically understudied, with no dedicated long-term climate assessments despite its high sensitivity to temperature shifts, monsoon irregularities, and increasing climate-driven hazards; the absence of local stations and scientific investigations has created a major research gap in understanding multivariate climate behavior in this remote Himalayan region. To address this gap, the study aims to analyze four decades of historical climate variability, diagnose interrelationships among key climatic variables, identify long-term trends and shifts in temperature, precipitation, solar radiation, humidity, wind, and cloud cover, and finally develop CMIP6 SSP245-driven climate pathways projecting conditions through 2050. The main objective is to generate a localized, scientifically robust climate profile that supports evidence-based adaptation planning for communities in Chhedagad.\u003c/p\u003e"},{"header":"2. Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eChhedagad Municipality is situated in the south-central part of Jajarkot District within Karnali Province, Nepal, covering an area of approximately 284.20 km\u003cb\u003e\u0026sup2;\u003c/b\u003e and accommodating a population of 37,877 according to the 2021 national census (\u003cem\u003ePopulation | National Population and Housing Census 2021 Results\u003c/em\u003e, n.d.). Administratively, it comprises 13 wards, formed after the nationwide restructuring of 2017 through the merger of the former Salma, Dasera, Suwanauli, Pajaru, Jhapra, and Karkigaun Village Development Committees, with Karkigaun designated as the municipal headquarters.(\u003cem\u003eChhedagad Municipality, Karkigaun, Jajarkot, Karnali Province, Nepal. | \u0026ldquo;Prosperous Chhedagad, Our Future,\u0026rdquo;\u003c/em\u003e n.d.) Geographically, the municipality lies within the mid-hill physiographic belt of the Himalayas, characterized by rugged terrain, steep slopes, variable elevations, and deeply incised river valleys typical of Jajarkot District (\u003cem\u003eJajarkot Topographic Map, Elevation, Terrain\u003c/em\u003e, n.d.). This landscape is influenced by the Mahabharat Range and contributes to pronounced micro-climatic variation, where temperature, precipitation, and other meteorological parameters fluctuate across short spatial distances.\u003c/p\u003e \u003cp\u003eThe municipality is bordered by Junichande Rural Municipality to the north and northwest, Barekot and Kuse areas toward the central and eastern highlands of Jajarkot, and Simta and Bheri regions toward the south, situating Chhedagad as a transitional zone between higher Himalayan ridges and warmer southern valleys, as shown in the attached map. The study area represents a typical mid-hill settlement dependent on agriculture, rainfall variability, and natural water systems. Because of limited meteorological stations and its rugged topography, climate information for Chhedagad relies largely on gridded and satellite-derived datasets such as NASA POWER and CMIP6 products. These conditions make the municipality an important site for assessing long-term hydro-meteorological variability, understanding local climatic sensitivities, and projecting future changes under warming scenarios\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e \u003cp\u003eThis study relies on a combination of observational climate datasets and global climate model simulations to examine multivariate hydro-meteorological behavior in Chhedagad Municipality. Historical climate data for the period 1984\u0026ndash;2024 was obtained from the NASA POWER database (\u003cem\u003eNASA POWER | Data Access Viewer (DAV)\u003c/em\u003e, n.d.), which provides satellite-derived, bias-adjusted meteorological parameters with global consistency. These data are particularly valuable for areas such as Jajarkot District, where ground-based climate stations are sparse and long-term observational records are limited. The variables extracted from NASA POWER include monthly mean, maximum, and minimum air temperature, total precipitation, relative and specific humidity, surface pressure, solar radiation, cloud amount, and wind speed. These variables collectively represent the principal drivers of local climate processes and provide a comprehensive basis for multivariate climate diagnostics.\u003c/p\u003e \u003cp\u003eTo explore future climate pathways, projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under the SSP2-4.5 (SSP245) scenario were incorporated into the analysis. Downscaled and bias-corrected model outputs available through the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) archive were used to obtain future monthly climate data at a spatial resolution of 0.25\u0026deg;. This product integrates bias correction and statistical downscaling, allowing for improved representation of climate characteristics in complex mountainous terrain. Only the grid cells corresponding to the centroid and spatial footprint of Chhedagad Municipality were selected to generate a future climate series for 2025\u0026ndash;2050. The SSP245 scenario, representing a stabilization pathway with moderate emissions, was chosen because of its relevance to near- and mid-century regional climate planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Preprocessing and Quality Control\u003c/h2\u003e \u003cp\u003eBefore analysis, all datasets underwent a structured preprocessing workflow to ensure temporal consistency and analytical reliability. Raw CSV files were inspected for missing values, formatting inconsistencies, and non-numeric entries, which were corrected or removed as necessary. Column names were standardized, and the month-wise datasets were reshaped into continuous time-series formats suitable for long-term trend assessment. The year 1983 was excluded because several variables contained incomplete or unreliable entries, and the analysis period was therefore defined as 1984\u0026ndash;2024. From the cleaned dataset, monthly, seasonal, and annual means were computed, and a climatological baseline spanning 1984\u0026ndash;2014 was established to evaluate anomalies.\u003c/p\u003e \u003cp\u003eTo ensure comparability between observational datasets and future climate projections, CMIP6 SSP245 model outputs were harmonized with the NASA POWER observations. This adjustment preserved long-term trends while aligning the model\u0026rsquo;s mean state with observed climatic conditions. The harmonized dataset enabled the construction of a continuous historical\u0026ndash;future climate series for all key hydro-meteorological variables. All processing steps were conducted using Python libraries such as Pandas, NumPy, and xarray, ensuring reproducibility and transparency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Trend Analysis and Multivariate Climate Diagnostics\u003c/h2\u003e \u003cp\u003eLong-term climate behavior in Chhedagad Municipality was examined through a combination of statistical trend analysis and multivariate diagnostic techniques. Historical patterns were characterized by computing anomalies relative to the climatological baseline, allowing identification of long-term warming, cooling, wetting, drying, and humidity-related shifts over the study period. To detect monotonic changes in meteorological variables, the non-parametric Mann-Kendall test was applied, while the Sen\u0026rsquo;s slope estimator quantified the magnitude of change per year. These methods enabled the evaluation of gradual long-term trends without assuming linearity or normally distributed data, making them appropriate for the climatic context of mountainous Nepal.\u003c/p\u003e \u003cp\u003eIn addition to trend assessment, interconnections among climatic variables were examined to understand how temperature, precipitation, humidity, cloud cover, wind speed, and solar radiation co-vary through time. Pearson correlation matrices and time-series alignment techniques were used to interpret cross-variable relationships, such as how cloud amount influences solar radiation, how humidity interacts with temperature, or how pressure variability corresponds to seasonal atmospheric transitions. The integration of these diagnostics provides a deeper understanding of the coupled nature of climate processes in the municipality and establishes a foundation for interpreting projected mid-century changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Future Climate Projection Framework\u003c/h2\u003e \u003cp\u003eFuture climate trajectories for 2025\u0026ndash;2050 were derived from the downscaled CMIP6 SSP245 dataset, which incorporates the physical principles of global climate models while providing local-scale climatic relevance through statistical downscaling. After harmonization with observed records, the projected monthly values for temperature, precipitation, humidity, solar radiation, and other variables were appended to the historical dataset to form a continuous time series extending to 2050. The projected data were analyzed using the same diagnostic tools applied to historical observations, allowing direct comparison of past and future climatic conditions. This approach retains the physical credibility of GCM-based projections while allowing them to be interpreted in the context of observed climatic variability and long-term regional trends.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Temperature Variability in Chhedagad Municipality (1984\u0026ndash;2024)\u003c/h2\u003e \u003cp\u003eThe annual mean temperature series for all thirteen wards (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) exhibit a clear interannual fluctuation superimposed on a gradual warming tendency across Chhedagad Municipality. Wards 1, 12, and 13 represent the warmest zones, with annual means consistently exceeding\u0026thinsp;~\u0026thinsp;20\u0026deg;C, whereas the remaining wards cluster between ~\u0026thinsp;12\u0026ndash;14\u0026deg;C, indicating strong elevation-controlled thermal gradients within the municipality. Despite year-to-year variability, all wards display a similar temporal structure: warmer phases around the mid-1990s and early 2000s, followed by a short-term cooling dip around 2010\u0026ndash;2015, and a renewed warming trend approaching 2020\u0026ndash;2023. This synchronicity across wards suggests that regional-scale climate forcing dominates over local microscale influences\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMonthly climatological temperature profiles for all wards (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) demonstrate a coherent seasonal cycle driven by the South Asian monsoon system. Temperatures begin increasing rapidly from March, peak during May-June, and decline steadily afterward, reaching their minima during December-January. The warmest wards (1, 12, 13) show peak monthly temperatures above 26\u0026deg;C, whereas the remaining wards peak near 17\u0026ndash;19\u0026deg;C. Despite differences in magnitude, the seasonal shape remains consistent across wards, emphasizing spatial uniformity in seasonal heating and cooling patterns. Notably, the shoulder seasons (March-April and October-November) are transitional and reflect moderate thermal conditions, highlighting periods of climatic sensitivity relevant for agriculture and water-resource planning.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMonthly climatology demonstrates a coherent seasonal cycle in all wards, with temperatures rising steadily from January to a pre-monsoon peak in May or June and declining toward December. Lower-elevation wards reach peak temperatures of about 26\u0026ndash;28\u0026deg;C, while mid-elevation wards peak between 16\u0026deg;C and 20\u0026deg;C. These seasonal and long-term patterns together indicate a consistent warming signal across the municipality, forming an essential baseline for evaluating future temperature changes under CMIP6 SSP245 projections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Precipitation Variability in Chhedagad Municipality (1984\u0026ndash;2024)\u003c/h2\u003e \u003cp\u003eAnnual precipitation patterns across the 13 wards of Chhedagad Municipality exhibit a clear upward trajectory over the 1984\u0026ndash;2024 period when expressed as annual mean precipitation in mm/day (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the early decades, most wards recorded annual mean values between 1.5\u0026ndash;2.2 mm/day, representing a comparatively drier hydroclimatic baseline. Beginning around the early 2000s, however, nearly all wards show a substantial rise in mean precipitation, with values frequently reaching 3.0-4.5 mm/day in recent years. This increase is consistent across wards, indicating a municipality-wide intensification of rainfall rather than isolated variability. The post-2000 period also displays enhanced year-to-year fluctuations, suggesting increased hydrological instability and more variable monsoon performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMonthly precipitation climatology further confirms the strong monsoonal influence on the region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Across all wards, precipitation remains minimal during the winter and pre-monsoon months (January-April), with monthly mean values typically below 1 mm/day. A rapid escalation begins in May, culminating in a pronounced peak during July and August, where mean precipitation exceeds 8\u0026ndash;10 mm/day depending on the ward. This narrow monsoon window contributes the majority of annual rainfall, highlighting the municipality\u0026rsquo;s dependence on a short yet intense precipitation season. Following the peak, rainfall declines sharply from September onward, reaching dry-season levels again by November-December. The uniformity of the monthly cycle across wards indicates that large-scale atmospheric dynamics, rather than local variability, dominantly control seasonal rainfall distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Solar Radiation and Solar Energy Potential\u003c/h2\u003e \u003cp\u003eThe spatiotemporal analysis of solar radiation and solar energy potential across all 13 wards of Chhedagad Municipality reveals coherent multi-decadal variability and a strong seasonal cycle, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Annual solar radiation fluctuates between approximately 4.5 and 5.2 kWh/m\u0026sup2;/day from 1984 to 2024, with most wards exhibiting higher radiation levels during the late 1980s through early 2000s. Following 2005, nearly all wards display a declining tendency, with several dropping toward 4.5 kWh/m\u0026sup2;/day, marking some of the lowest values of the study period. A modest recovery is visible after 2020; however, radiation levels remain below earlier peak ranges, indicating a persistent long-term reduction in available solar irradiance.\u003c/p\u003e \u003cp\u003eThe monthly climatology in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e further confirms a consistent seasonal radiation pattern across all wards. Solar radiation peaks during March-May, reaching 6.2\u0026ndash;6.5 kWh/m\u0026sup2;/day, representing the highest energy influx to the surface. The lowest values occur in December\u0026ndash;January, typically declining to 3.5-4.0 kWh/m\u0026sup2;/day, showing more than a 40% reduction compared to pre-monsoon maxima. Radiation decreases sharply during the monsoon season (June\u0026ndash;September), stabilizing around 4.5-5.0 kWh/m\u0026sup2;/day, reflecting the influence of cloud cover and atmospheric moisture. The similarity of seasonal curves across all wards confirms that the municipality experiences a uniform solar regime driven predominantly by regional atmospheric processes rather than localized microclimate variability.\u003c/p\u003e \u003cp\u003eSolar energy potential, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, displays annual totals ranging from approximately 1,650 to 1,850 kWh/m\u0026sup2;/year, closely mirroring the temporal structure of solar radiation. Higher annual energy values dominate the period before 2005, with multiple wards consistently\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eexceeding 1,800 kWh/m\u0026sup2;/year. After 2005, most wards exhibit a downward trend, converging toward 1,650-1,700 kWh/m\u0026sup2;/year during the mid-2010s. Although a slight improvement appears after 2020, these values remain lower than earlier maxima, indicating a sustained reduction in solar energy availability over recent decades.\u003c/p\u003e \u003cp\u003eThe monthly energy distribution in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e emphasizes a distinct seasonal pattern consistent across all wards. Maximum monthly solar energy occurs during April-May, typically reaching 180\u0026ndash;200 kWh/m\u0026sup2;, while the minimum occurs in December-January, around 110\u0026ndash;130 kWh/m\u0026sup2;. Energy availability declines notably during the monsoon season, averaging 130\u0026ndash;150 kWh/m\u0026sup2;, corresponding to reduced radiation from cloud cover and atmospheric scattering. This seasonal structure suggests that pre-monsoon months offer the highest efficiency for solar-based power generation, while winter months remain the least productive.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTogether, these results demonstrate that Chhedagad Municipality experiences (i) a measurable long-term decline in both solar radiation and solar energy potential after the early 2000s, (ii) a strong and consistent seasonal cycle across all wards, and (iii) a high degree of spatial uniformity in solar behavior across the municipality. Despite recent declines, the annual energy potential of ~\u0026thinsp;1,650-1,850 kWh/m\u0026sup2;/year confirms that the region retains favorable conditions for solar energy development, particularly during the pre-monsoon period when resource availability is at its peak.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Humidity Characteristics (Specific and Relative Humidity)\u003c/h2\u003e \u003cp\u003eThe analysis of humidity patterns across all 13 wards of Chhedagad Municipality (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) reveals distinct long-term variability and a robust seasonal structure in both specific humidity (g/kg) and relative humidity (%). Although specific humidity values remain comparatively low throughout the study period, both parameters exhibit coherent temporal behavior driven by temperature cycles and moisture availability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnnual specific humidity remains consistently between 2.5 and 6.0 g/kg across all wards, showing a gradual increasing tendency toward recent decades. Most wards exhibit a modest rise from the 1980s through the 2000s, followed by slight interannual fluctuations without any major decline. This upward drift aligns with the observed warming trend in the region (discussed earlier), as higher air temperatures enhance moisture-holding capacity. Despite the relatively small magnitude of specific humidity, its year-to-year variability is smooth and synchronous across the municipality, indicating that large-scale atmospheric moisture patterns, rather than local microclimatic differences, dominate humidity processes.\u003c/p\u003e \u003cp\u003eRelative humidity shows more pronounced variability, with annual values ranging between 45% and 70% across wards. In nearly all wards, relative humidity exhibits a clear increasing pattern, particularly after the late 1990s, gradually rising toward 60\u0026ndash;70% in the most recent decade. This long-term shift is visible consistently across the municipality, suggesting increasing atmospheric moisture presence or reduced evaporative demand in recent years. Interannual fluctuations are common, with minor dips corresponding to warmer and drier years, but the overall trajectory is upward across the entire 40-year period.\u003c/p\u003e \u003cp\u003eMonthly climatology further clarifies humidity seasonality. Across all wards, specific humidity reaches its maximum during June, August, typically rising to 5\u0026ndash;6 g/kg, driven by monsoonal moisture influx. Minimum values occur during December-January, where specific humidity frequently drops to 2.5-3.0 g/kg, coinciding with the cold and dry winter season. Relative humidity shows a similar seasonal pattern but with stronger amplitude: values peak during the monsoon (70\u0026ndash;80%) and decline to 45\u0026ndash;55% during the winter months. This consistent structure across all 13 wards reinforces the dominant influence of regional monsoon circulation in shaping humidity conditions.\u003c/p\u003e \u003cp\u003eOverall, the combined annual and monthly analyses demonstrate that Chhedagad Municipality has experienced: (i) a steady long-term increase in both relative and specific humidity, (ii) strong seasonal humidity cycles governed by monsoon rainfall and winter dryness, and (iii) high spatial uniformity across all wards, confirming that humidity dynamics are primarily controlled by large-scale climatic processes rather than local variability. These findings provide a robust baseline for interpreting moisture-related climate impacts, hydrological responses, and future projections within the municipality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Wind Speed Variability\u003c/h2\u003e \u003cp\u003eThe long-term and seasonal wind speed characteristics across the 13 wards of Chhedagad Municipality (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) exhibit clear temporal variability and a strong annual cycle consistent throughout the study period. Annual mean wind speeds generally range between 2.1 and 2.6 m/s, with substantial interannual fluctuations but no evidence of a persistent long-term increase. Instead, most wards display a gradual declining tendency after the early 2000s, where peak wind speeds often exceeded 2.5 m/s, followed by values frequently dropping toward 2.2\u0026ndash;2.3 m/s in the last decade. This downward shift is consistently visible across multiple wards, indicating a municipality-wide reduction in average wind intensity during recent years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterannual variability is pronounced in several wards, with short-term increases occasionally interrupting the broader declining pattern. For example, mid-1990s to early-2000s peaks approach or surpass 2.5 m/s in nearly all wards, while the period after 2010 shows fewer years exceeding 2.3\u0026ndash;2.4 m/s, suggesting a moderation of wind activity. Despite these fluctuations, spatial uniformity is strong, as all 13 wards exhibit similar annual wind speed structures, implying control by regional atmospheric dynamics rather than localized topographic influences.\u003c/p\u003e \u003cp\u003eMonthly climatology in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e confirms a well-defined seasonal cycle. Wind speeds peak during March-May, where values rise to 2.6-3.0 m/s, representing the period of maximum air movement preceding the monsoon. The lowest wind speeds occur during October\u0026ndash;December, commonly falling to 1.9\u0026ndash;2.1 m/s, marking a reduction of nearly 30\u0026ndash;40% from pre-monsoon maxima. During the monsoon months (June-August), wind speeds decline moderately but remain higher than winter levels, indicating sustained but stabilized atmospheric flow during this season. This seasonal pattern is consistent across all wards, demonstrating strong coherence in wind behavior throughout the municipality.\u003c/p\u003e \u003cp\u003eOverall, the wind speed dataset reveals three key findings: (i) a moderate long-term decline in annual mean wind speed since the early 2000s, with most wards shifting from historical averages near 2.5 m/s toward 2.2\u0026ndash;2.3 m/s in recent years, (ii) a robust and uniform seasonal cycle, characterized by strong pre-monsoon winds, moderated monsoon winds, and minimal post-monsoon and winter wind activity and, (iii) high spatial consistency across all 13 wards, indicating dominant control by regional-scale atmospheric circulation rather than local terrain effects. These results provide essential insights into changing wind dynamics within the municipality and offer a quantitative basis for evaluating wind-dependent environmental processes and renewable energy planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Cloud Amount Variability\u003c/h2\u003e \u003cp\u003eThe annual and monthly cloud amount distribution across all 13 wards of Chhedagad Municipality (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) demonstrates distinct long-term fluctuations and a pronounced seasonal cycle. Annual cloud amount generally varies between 40% and 60%, with consistent spatial agreement across all wards. Several wards show elevated cloudiness between the late 1990s and early 2010s, where annual values frequently rise to 55\u0026ndash;60%, indicating a period of persistently higher atmospheric moisture and cloud cover. After 2015, most wards exhibit a declining tendency, with values commonly returning to the 45\u0026ndash;50% range. Despite this decline, interannual variability remains evident, reflecting the influence of regional circulation patterns and monsoon dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMonthly cloud climatology reveals a strong seasonal structure, with cloud cover peaking during the monsoon months (June-August) when values reach 75\u0026ndash;85%, constituting the cloudiest period of the year across all wards. This pronounced peak reflects deep convective activity and sustained moisture influx during the South Asian monsoon. In contrast, the lowest cloud amounts occur in the post-monsoon and winter months (November-January), when values decrease to 35\u0026ndash;45%, indicating clearer skies, reduced convection, and drier atmospheric conditions. The transition periods, pre-monsoon (March-May) and early post-monsoon (September-October), show moderate cloud levels, typically between 45% and 60%.\u003c/p\u003e \u003cp\u003eThe spatial consistency of cloud amount across all wards indicates that cloud formation in the municipality is predominantly driven by large-scale synoptic and seasonal climatic processes rather than local topographic variation. Annual cloud behavior closely aligns with observed trends in humidity and solar radiation, where years with higher cloud amount correspond to reduced radiation availability. The sustained monsoon peak and winter minimum observed in all wards demonstrate a uniform atmospheric response to regional seasonal forcing.\u003c/p\u003e \u003cp\u003eOverall, cloud amount in Chhedagad Municipality is characterized by (i) substantial year-to-year fluctuations, (ii) a well-defined monsoon-dominated seasonal cycle, and (iii) high spatial coherence across wards. These factors play a critical role in modulating solar radiation, temperature variability, and local hydrometeorological processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Multivariate Correlation Structure of Climatic Variables\u003c/h2\u003e \u003cp\u003eThe correlation matrices for all 13 wards of Chhedagad Municipality (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e) reveal a highly consistent multivariate structure, highlighting strong physical linkages among temperature, precipitation, solar radiation, cloud amount, and humidity variables. Despite geographic differences among wards, the correlation patterns demonstrate remarkable spatial uniformity, indicating that regional atmospheric processes drive climatic interactions across the municipality.\u003c/p\u003e \u003cp\u003eA dominant relationship is the strong negative correlation between solar radiation and cloud amount, with coefficients typically ranging from \u0026minus;\u0026thinsp;0.92 to -0.97, representing one of the strongest associations in the dataset. This inverse relationship reflects the fundamental radiative effect of clouds, where increased cloud cover significantly reduces incoming solar radiation. This consistent and high-magnitude correlation across all wards verifies the strong regulatory role of cloudiness in modulating the surface energy budget.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTemperature shows a moderate to strong negative correlation with precipitation (approximately \u0026minus;\u0026thinsp;0.35 to -0.45), indicating that wetter years and seasons tend to be cooler across the municipality. Conversely, temperature shows a positive correlation with solar radiation (around 0.30 to 0.45), demonstrating that clearer, less cloudy conditions are associated with higher surface heating. These relationships align with established climatological behavior and further validate the physical coherence of the dataset.\u003c/p\u003e \u003cp\u003eSpecific humidity (SHUM) exhibits one of the strongest internal consistencies across wards, showing a near-perfect positive correlation with relative humidity (RHUM) (typically 0.96\u0026ndash;0.98). This reflects their shared dependence on atmospheric moisture content. SHUM also displays a moderate positive correlation with precipitation (~\u0026thinsp;0.32\u0026ndash;0.45), indicating that higher atmospheric moisture is linked to wetter conditions. At the same time, SHUM correlates negatively with solar radiation (approximately \u0026minus;\u0026thinsp;0.35 to -0.45), reinforcing the role of cloudy, moisture-rich environments in reducing solar flux.\u003c/p\u003e \u003cp\u003eRelative humidity presents a correlation pattern similar to specific humidity but with slightly stronger negative relationships with temperature (\u0026asymp; -0.48 to -0.55). This behavior reflects the thermodynamic relationship whereby warmer conditions decrease relative moisture saturation unless compensated by increased water vapor. Likewise, RHUM maintains a moderate positive correlation with precipitation (\u0026asymp;\u0026thinsp;0.33\u0026ndash;0.46), supporting its sensitivity to rainfall events and moist air masses.\u003c/p\u003e \u003cp\u003eCloud amount also demonstrates consistent correlations across all wards. In addition to its strong negative relationship with solar radiation, cloud amount shows a positive correlation with precipitation (~\u0026thinsp;0.40\u0026ndash;0.52) and moderate negative correlation with temperature (~ -0.35 to -0.45). These relationships confirm that cloudy years tend to be wetter and cooler, a hallmark of monsoon-driven climates.\u003c/p\u003e \u003cp\u003eOverall, the correlation results exhibit four major scientifically supported outcomes (i) cloud amount is the primary regulator of surface solar energy, showing the strongest negative correlations across all wards., (ii) temperature decreases under wetter, cloudier, and moisture-rich conditions, demonstrating coherent thermodynamic behavior, (iii) specific humidity and relative humidity form a tightly linked moisture pair, showing near-perfect correlation throughout the municipality, and (iv) the correlation structure is spatially uniform, implying that climatic drivers affect all wards similarly, with minimal local deviation. These robust and physically consistent relationships provide a strong statistical foundation for interpreting climatic interactions in Chhedagad Municipality and support subsequent modeling and diagnostic analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Projected Temperature Dynamics (2025\u0026ndash;2050)\u003c/h2\u003e \u003cp\u003eThe CMIP6 SSP2-4.5 driven projections indicate that Chhedagad Municipality is expected to experience a persistently warm thermal regime throughout 2025\u0026ndash;2050, with notable interannual variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea). The annual mean temperature fluctuates between ~\u0026thinsp;12.5\u0026deg;C and 14.5\u0026deg;C, showing several alternating warm and cool years rather than a strictly linear trend. Peaks around 2034, 2041, and 2047 suggest episodes of intensified warming, while dips around 2030, 2037, and 2043 indicate short-term cooling anomalies. Despite this variability, the overall trajectory suggests a gradually warming climate, with the latter half of the projection period generally warmer than the early 2030s. These fluctuations likely reflect the interaction of large-scale climate dynamics superimposed on a steadily warming baseline under SSP245 conditions\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe projected monthly climatology (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb) reveals a well-defined seasonal temperature cycle, mirroring the historical monsoon-driven thermal pattern but at elevated values. Winter months (December-February) remain the coolest of the year, ranging between 5\u0026ndash;7\u0026deg;C, while spring months show a rapid warming transition, with temperatures rising to 11\u0026deg;C in March and surpassing 15\u0026deg;C by May. The warmest period is projected for June-August, with mean temperatures peaking at approximately 19-19.5\u0026deg;C in June. Following this summer peak, temperatures gradually decline through September and fall sharply after October, reaching\u0026thinsp;~\u0026thinsp;7\u0026deg;C by December. This seasonal structure indicates that future warming is strongest in the pre-monsoon and monsoon months, which aligns with typical regional patterns of enhanced heat accumulation and reduced evaporative cooling under climate change scenarios.\u003c/p\u003e \u003cp\u003eTogether, these annual and seasonal projections highlight a future climate characterized by higher mean temperatures, amplified seasonal contrasts, and notable interannual variability. Such warming trends have important implications for local hydrology, agriculture, and heat-related hazards in Chhedagad Municipality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Projected Precipitation Dynamics (2025\u0026ndash;2050)\u003c/h2\u003e \u003cp\u003eThe CMIP6 SSP2-4.5 precipitation projections indicate substantial interannual variability in future rainfall across Chhedagad Municipality, with no clear monotonic trend but evident fluctuations around a moderately increasing baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea). Annual mean precipitation ranges approximately between 2.6 and 5.3 mm/day, reflecting alternating wet and dry years throughout the projection period. Several years, including 2034, 2040, 2042, and 2047, exhibit relatively elevated precipitation, while years such as 2030, 2036, and 2044 show noticeable reductions. This pattern suggests that while the overall rainfall regime remains variable, episodic high-rainfall years may become more frequent under mid-century climate conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe projected monthly climatology (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eb) reveals a strongly seasonal precipitation cycle that mirrors the monsoon-dominated historical pattern but at slightly elevated values. Precipitation remains minimal during the winter and pre-monsoon period (January-April), generally below 1 mm/day. A rapid increase begins in May, culminating in a distinct monsoon peak between July and August, where mean precipitation rises to approximately 15\u0026ndash;18 mm/day, representing the wettest period of the year. Following the monsoon peak, rainfall declines sharply after September, returning to dry-season conditions by November-December, with monthly means near or below 1 mm/day. This seasonal distribution highlights the continued dominance of the summer monsoon in shaping the municipality\u0026rsquo;s hydrological regime, with the mid-century climate maintaining a pronounced seasonal contrast.\u003c/p\u003e \u003cp\u003eTogether, the annual and monthly projections point to a future characterized by marked year-to-year variability and intensified monsoon season rainfall, reinforcing the need for adaptive water-resource planning, flood preparedness, and climate-resilient agricultural strategies in Chhedagad Municipality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Projected Specific and Relative Humidity (2025\u0026ndash;2050)\u003c/h2\u003e \u003cp\u003eThe CMIP6 SSP2-4.5 projections indicate a coherent increase in atmospheric moisture content over Chhedagad Municipality during 2025\u0026ndash;2050 (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003ea). Annual specific humidity fluctuates between 4\u0026ndash;7 g/kg, showing recurrent humid anomalies in the early 2030s and late 2040s. Relative humidity varies between 55\u0026ndash;70%, with modest interannual oscillations that parallel specific-humidity fluctuations. Although neither variable displays a strictly monotonic trend, the sustained higher-moisture years during mid-century suggest an enhanced capacity of the atmosphere to store water vapor. This behavior is consistent with thermodynamic expectations under warming scenarios, where increased temperature supports greater moisture-holding capacity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe monthly climatology (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003eb) reveals a seasonally stratified humidity regime. Specific humidity exhibits a clear minimum during December-February (\u0026asymp;\u0026thinsp;3\u0026ndash;4 g/kg), rises sharply with pre-monsoon warming, and peaks at 7\u0026ndash;8 g/kg during July-August, coinciding with pronounced monsoonal moisture influx. Relative humidity follows a similarly phased pattern, with maxima during the monsoon core and suppressed values during the late winter and pre-monsoon months. Together, these projections indicate that mid-century atmospheric moisture dynamics will continue to be dominated by monsoon-driven processes, with implications for convective rainfall potential and local evapotranspiration fluxes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.11 Projected Solar Radiation (2025\u0026ndash;2050)\u003c/h2\u003e \u003cp\u003eProjected all-sky surface shortwave radiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ea) demonstrates moderate interannual variability, with annual means spanning 4.8\u0026ndash;6.2 kWh/m\u0026sup2;/day across the projection period. Years such as 2034, 2040, and 2047 show enhanced radiative loading, whereas 2030 and 2037 show comparatively reduced surface radiation, indicating periods of increased atmospheric cloudiness or aerosol optical effects. The absence of a persistent upward or downward long-term trajectory suggests that the CMIP6 SSP245 scenario maintains relatively stable radiative atmospheric conditions over mid-century.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMonthly mean solar radiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003eb) preserves the strong seasonal cycle characteristic of Himalayan foothill climates. Radiation is lowest in December\u0026ndash;January (\u0026asymp;\u0026thinsp;3\u0026ndash;4 kWh/m\u0026sup2;/day), rises abruptly through the pre-monsoon months to peak in April-May at 7\u0026ndash;8 kWh/m\u0026sup2;/day, and then declines during the monsoon season due to high cloud cover. This seasonal asymmetry underscores the persistence of strong pre-monsoon solar forcing, which remains a critical window for energy harvesting and land-surface heating processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.12 Projected Solar Energy Potential (2025\u0026ndash;2050)\u003c/h2\u003e \u003cp\u003eConversion of daily shortwave radiative fluxes into monthly solar-energy potential highlights significant seasonal and interannual variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003ea). Annual solar energy potential ranges between 140\u0026ndash;170 kWh/m\u0026sup2;/month, with high-yield years clustering around the mid-2030s and late 2040s. Such multi-year fluctuations carry practical significance for long-term off-grid solar system performance, particularly in remote municipalities where energy supply security is sensitive to climatic variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe monthly projection (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003eb) shows a clear concentration of solar-energy availability in the pre-monsoon months. April and May consistently exceed 200 kWh/m\u0026sup2;/month, representing the dominant contribution to annual solar energy. In contrast, July\u0026ndash;August exhibit significant reductions due to monsoon cloud cover, and December\u0026ndash;January represent the climatological minimum (\u0026lt;\u0026thinsp;110 kWh/m\u0026sup2;/month). These patterns reinforce the need for seasonal load-management strategies and potential hybridization with wind or hydropower to ensure consistent year-round renewable energy supply.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.13 Projected Wind Speed (2025\u0026ndash;2050)\u003c/h2\u003e \u003cp\u003eProjected 10-m wind speeds (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003ea) reveal modest but meaningful interannual variability across the projection period. Annual mean wind speeds fluctuate between 2.8 and 4.0 m/s, with intermittent wind maxima in 2034, 2041, and 2047, and relative minima near 2030 and 2038. Such oscillatory behavior reflects the influence of regional synoptic patterns rather than long-term structural change in the wind regime under SSP245. Nonetheless, the maintenance of moderate wind speeds is favorable for small-scale hybrid renewable energy systems.\u003c/p\u003e \u003cp\u003eThe monthly climatology (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e17\u003c/span\u003eb) displays a distinct seasonal cycle. Wind speeds are weakest in December\u0026ndash;January, strengthen rapidly during the pre-monsoon period, and peak in May-June at 4-4.5 m/s. During the monsoon, speeds moderate slightly due to increased atmospheric moisture and stability, before declining in the post-monsoon season. The persistence\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eof strong pre-monsoon winds suggests potential opportunities for integrating wind-assisted energy generation during critical months of high demand and variable solar availability.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides the first comprehensive multivariate climate assessment for Chhedagad Municipality, integrating four decades of satellite-derived observations (1984\u0026ndash;2024) with CMIP6 SSP2-4.5 projections to 2050. The results reveal a spatially coherent climate system dominated by strong monsoonal forcing, elevation-controlled temperature gradients, and consistent inter-variable relationships that operate uniformly across all 13 wards. The diagnostic patterns observed in Chhedagad parallel broader climate trajectories documented for the Himalayan mid-hills, confirming that even remote municipalities experience regionally synchronized hydro-meteorological variability.\u003c/p\u003e \u003cp\u003eHistorical temperature records indicate a gradual warming tendency, particularly in lower-elevation wards (1, 12, and 13), which maintained annual averages exceeding\u0026thinsp;~\u0026thinsp;20\u0026deg;C throughout the study period. The warm\u0026ndash;cool oscillations, mid-1990s/early-2000s warm phases, and cooling dips around 2010\u0026ndash;2015 resemble previously reported regional patterns associated with large-scale climate variability, including ENSO-modulated monsoon fluctuations. Despite local topographic complexity, the thermal regime remained highly coherent across wards, underscoring the dominance of synoptic processes over microscale terrain effects.\u003c/p\u003e \u003cp\u003ePrecipitation diagnostics reveal a distinct intensification of annual rainfall beginning in the early 2000s, accompanied by enhanced interannual variability, a hallmark of emerging monsoon instability noted across Nepal\u0026rsquo;s mid-western region. The monsoon peak (July-August) contributes the majority of annual rainfall, whereas the winter and pre-monsoon months remain markedly dry. This pronounced seasonality has significant implications for agriculture, slope stability, and water-security planning, particularly as rainfall extremes intensify under ongoing climate change.\u003c/p\u003e \u003cp\u003eHumidity, cloud amount, wind speed, and solar radiation collectively demonstrate a physically consistent and tightly coupled climate system. Rising humidity levels, both specific and relative, align with warmer baseline temperatures and expanding atmospheric moisture capacity. Cloud amount shows clear monsoon-season dominance and is strongly inversely correlated with solar radiation (r \u0026asymp; -0.92 to -0.97), confirming its central role in regulating surface energy availability. Declining solar radiation and energy potential after the early 2000s appear linked to increased cloud cover and aerosol effects, consistent with regional dimming observed across South Asia. Meanwhile, wind speeds display a gradual weakening trend, reducing pre-monsoon maxima from \u0026gt;\u0026thinsp;2.5 m/s historically to ~\u0026thinsp;2.2\u0026ndash;2.3 m/s in recent years, which may reflect evolving regional circulation patterns. The multivariate correlation analyses reinforce the physical coherence of these findings. Temperature decreases during wetter, cloudier years; humidity strongly covaries with precipitation; and cloud amount governs both radiative fluxes and thermal responses. The near-identical correlation structure across all wards indicates shared atmospheric control rather than localized variability, highlighting the municipality\u0026rsquo;s climatic unity despite its rugged terrain.\u003c/p\u003e \u003cp\u003eFuture projections under SSP2-4.5 extend these patterns into mid-century. Temperatures are expected to rise gradually, particularly during pre-monsoon and monsoon months, increasing heat exposure and evapotranspirative demand. Precipitation remains highly variable, with episodic wet years possibly becoming more frequent\u0026mdash;mirroring global model expectations for a more moisture-laden monsoon regime. Humidity projections suggest persistently moist atmospheric conditions, which may exacerbate heat stress and enhance convective potential. Solar radiation and energy potential remain seasonally reliable but fluctuating, with pre-monsoon months offering the highest renewable-energy potential. Wind projections exhibit modest interannual variability but retain a clear seasonal structure, with implications for hybrid energy planning.\u003c/p\u003e \u003cp\u003eOverall, the study reveals a climate system characterized by increasing warmth, intensified monsoon rainfall, rising atmospheric moisture, reduced solar fluxes compared to earlier decades, and weakening winds, patterns consistent with regional Himalayan climate transformation. These findings underscore the need for localized, evidence-based adaptation strategies in agriculture, water management, and renewable energy design.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides the first comprehensive multivariate climate assessment for Chhedagad Municipality, revealing a climate system that is warming, increasingly moisture-laden, and marked by intensifying monsoon rainfall and rising hydro-climatic variability. The integration of four decades of NASA POWER observations with CMIP6 SSP2-4.5 projections demonstrates that temperature has gradually increased across all 13 wards, precipitation has strengthened and become more irregular after the early 2000s, and atmospheric moisture, expressed through both specific and relative humidity, has risen in parallel with these changes. Declining solar radiation and weakening wind speeds since the mid-2000s indicate broader shifts in regional atmospheric processes, while the strong and spatially uniform correlations among cloud amount, radiation, humidity, and temperature confirm that large-scale monsoon dynamics dominate the municipality\u0026rsquo;s climate behavior. Future projections to 2050 suggest continued warming, enhanced monsoonal moisture influx, and sustained interannual variability across temperature, rainfall, humidity, solar energy, and wind regimes. Together, these findings highlight a transition toward a warmer, wetter, and more variable mid-century climate, underscoring the need for evidence-based adaptation strategies in agriculture, water-resource management, energy planning, and hazard preparedness. By establishing a robust historical baseline and a reliable future trajectory, this study fills a critical knowledge gap for Chhedagad and provides essential scientific guidance for local climate-resilient development.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah T, Bashir J, Romshoo SA (2025) Assessing glacier changes and hydrological impacts in the upper Indus Basin under CMIP6 climate scenarios. iScience 28(8):113200. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.isci.2025.113200\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2025.113200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBastola S, Cho J, Kam J, Jung Y (2024) Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models. Atmos Res 311:107720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosres.2024.107720\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosres.2024.107720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChhedagar Municipality (2025), \u003cem\u003eKarkigaun, Jajarkot, Karnali Province, Nepal. | Prosperous Chhedagar, Our Future.\u003c/em\u003e (n.d.). Retrieved November 29, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://chhedagadmun.gov.np/\u003c/span\u003e\u003cspan address=\"https://chhedagadmun.gov.np/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColston JM, Ahmed T, Mahopo C, Kang G, Kosek M, De Sousa Junior F, Shrestha PS, Svensen E, Turab A, Zaitchik B (2018) Evaluating meteorological data from weather stations, and from satellites and global models for a multi-site epidemiological study. Environ Res 165:91\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2018.02.027\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2018.02.027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDahal P, Shrestha ML, Panthi J, Pradhananga D (2020) Modeling the future impacts of climate change on water availability in the Karnali River Basin of Nepal Himalaya. Environ Res 185:109430. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2020.109430\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2020.109430\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhakal S, Gautam Y, Bhattarai A (2020) Evaluation of Temperature-Based Empirical Models and Machine Learning Techniques to Estimate Daily Global Solar Radiation at Biratnagar Airport, Nepal. Adv Meteorol 2020(1):8895311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2020/8895311\u003c/span\u003e\u003cspan address=\"10.1155/2020/8895311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eJajarkot topographic map, elevation, terrain\u003c/em\u003e. (n.d.). Topographic Maps. Retrieved November 29, (2025) from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://en-gb.topographic-map.com/map-z1xptj/Jajarkot/\u003c/span\u003e\u003cspan address=\"https://en-gb.topographic-map.com/map-z1xptj/Jajarkot/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha AN, Bhattarai U, Lamichhane M, Mehan S, Ghimire S, Bhattarai PK (2025) Assessing hydropower production potential with multisite hydrological modeling in the Koshi river Basin, Nepal. Energy Convers Management: X 28:101283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ecmx.2025.101283\u003c/span\u003e\u003cspan address=\"10.1016/j.ecmx.2025.101283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi M, Dhital H (2023) Impacts of Climate Change on Temperature and Precipitation in Nepal: Projections and Bias Correction. J Sustain Environ Manage 2:203\u0026ndash;212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimzadeh S, Ahmadi A, Baldocchi D, Fisher JB (2025) Climate change has increased global evaporative demand except in South Asia. Commun Earth Environ. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43247-025-02959-x\u003c/span\u003e\u003cspan address=\"10.1038/s43247-025-02959-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamichhane M, Phuyal S, Mahato R, Shrestha A, Pudasaini U, Lama SD, Chapagain AR, Mehan S, Neupane D, Lamichhane M, Phuyal S, Mahato R, Shrestha A, Pudasaini U, Lama SD, Chapagain AR, Mehan S, Neupane D (2024) Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool. Sustainability 16(8). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su16083262\u003c/span\u003e\u003cspan address=\"10.3390/su16083262\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Wang C, Su F (2021) \u003cem\u003eEvaluation of CMIP6 Models over Two Third Pole Subregions with Contrasting Circulation Systems\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI-D-21-0214.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI-D-21-0214.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eNASA POWER | Data Access Viewer (DAV)\u003c/em\u003e. (n.d.). Retrieved December 7, (2025) from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://power.larc.nasa.gov/data-access-viewer/\u003c/span\u003e\u003cspan address=\"https://power.larc.nasa.gov/data-access-viewer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003ePopulation | National Population and and Housing Census 2021 Results\u003c/em\u003e. (n.d.). Retrieved November 29, (2025) from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://censusnepal.cbs.gov.np/results/population?province=6\u0026amp;district=65\u0026amp;municipality=4\u003c/span\u003e\u003cspan address=\"http://censusnepal.cbs.gov.np/results/population?province=6\u0026amp;district=65\u0026amp;municipality=4\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePradhananga S, Nepal S, Kamal SK, Hafeez M (2025) Climate change will exacerbate seasonal flow variability in the Karnali River basin: Implications for water, energy, and agriculture sectors. Hydrol Res 56(6):471\u0026ndash;491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2166/nh.2025.193\u003c/span\u003e\u003cspan address=\"10.2166/nh.2025.193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawat G (2025) Four-decade hydro-meteorological variability and machine-learning based weather projections (1983\u0026ndash;2050) for Nalphu village, Jajarkot, Nepal using XGboost. Res Square. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21203/rs.3.rs-8262924/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-8262924/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawat G, Khanal N, Adhikari PB (2025) Characterization and Morphological Analysis of Trio-Glacial Lakes (Sankha Lake, Kukur Lake, and Thakur Jyu Lake) of Barekot Patan, Jajarkot, Nepal. Contemp Research: Interdisciplinary Acad J 8(2):100\u0026ndash;116. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3126/craiaj.v8i2.86413\u003c/span\u003e\u003cspan address=\"10.3126/craiaj.v8i2.86413\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha A, Jain MK, Joshi P, Das S, Rawat NS (2025) Development of daily downscaled, bias-corrected CMIP6 climate datasets for estimating reference evapotranspiration (ETo) in South Asia. Sci Data 12(1):1879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-025-06149-4\u003c/span\u003e\u003cspan address=\"10.1038/s41597-025-06149-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShrestha E, Poudyal S, Ghimire A, Maharjan S, Lamichhane M, Mehan S (2025) Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal. Results Eng 25:104254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rineng.2025.104254\u003c/span\u003e\u003cspan address=\"10.1016/j.rineng.2025.104254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSigdel KP, Ghimire NP, Pandeya B, Dawadi B, Sigdel KP, Ghimire NP, Pandeya B, Dawadi B (2022) Historical and Projected Variations of Precipitation and Temperature and Their Extremes in Relation to Climatic Indices over the Gandaki River Basin, Central Himalaya. Atmosphere 13(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos13111866\u003c/span\u003e\u003cspan address=\"10.3390/atmos13111866\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubedi SR, Lamichhane M, Dhungana S, Chalise B, Bhattarai S, Chaulagain U, Khatiwada R (2024) Assessing the impact of climate change on streamflow in the Tamor River Basin, Nepal: An analysis using SWAT and CMIP6 scenarios. Discover Civil Eng 1(1):135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44290-024-00143-2\u003c/span\u003e\u003cspan address=\"10.1007/s44290-024-00143-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUllah S, You Q, Zhang Y, Bhatti AS, Ullah W, Hagan DFT, Ali A, Ali G, Jan MA, Khan SN, Ali A (2020) Evaluation of CMIP5 models and projected changes in temperatures over South Asia under global warming of 1.5 oC, 2 oC, and 3 oC. Atmos Res 246:105122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosres.2020.105122\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosres.2020.105122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Shen C, Ullah I, Curio J, Chen D (2024) Evaluating heat stress and occupational risks in the Southern Himalayas under current and future climates. Npj Clim Atmospheric Sci 7(1):211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41612-024-00764-5\u003c/span\u003e\u003cspan address=\"10.1038/s41612-024-00764-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Tri-Chandra Multiple campus","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Chhedagad Municipality, Multivariate Climate Diagnostics, NASA POWER, CMIP6 SSP2-4.5, Himalayan Climate, Future Projections","lastPublishedDoi":"10.21203/rs.3.rs-8570798/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8570798/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnticipating future hydro-meteorological risks under a warming climate requires an understanding of localized climate behavior in remote Himalayan municipalities. Using 40 years of satellite-derived observations (1984\u0026ndash;2024) from NASA POWER and mid-century projections (2025\u0026ndash;2050) from downscaled CMIP6 SSP2-4.5 data, this study offers the first thorough multivariate climate assessment for Chhedagad Municipality, Jajarkot. Historical analyses reveal a clear warming tendency across all 13 wards, strong elevation-dependent temperature gradients, and a post-2000 intensification of precipitation accompanied by heightened interannual variability. Humidity has increased steadily, while solar radiation and wind speeds show declining trends since the early 2000s, indicating shifts in atmospheric moisture, cloudiness, and circulation. A highly coherent climate system dominated by monsoon processes is revealed by multivariate correlation diagnostics. The amount of clouds has a significant influence on temperature and solar radiation, humidity is correlated with precipitation, and all variables show spatially uniform behavior across wards. Forecasts for the middle of the century show persistent variability in radiation and wind regimes, higher atmospheric moisture, sustained monsoonal enhancement, and ongoing warming. When taken as a whole, these findings show a shift toward a climate that is warmer, wetter, and more moisture-rich, with more hydro-climatic uncertainty and stronger seasonal contrasts. In addition to providing practical insights for climate-resilient planning in agriculture, water resources, energy, and hazard management, this study creates the first localized climate baseline for Chhedagad.\u003c/p\u003e","manuscriptTitle":"Multivariate Climate Diagnostics and CMIP6 SSP245-Driven Future Pathways for Chhedagad Municipality, Jajarkot, Nepal (1984-2050)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 10:02:38","doi":"10.21203/rs.3.rs-8570798/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7c66c9e-d60c-4fcd-9c59-32d5f83edd73","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60932337,"name":"Climatology"},{"id":60932338,"name":"Meteorology"},{"id":60932339,"name":"Climate Analysis and Modeling"},{"id":60932340,"name":"Hydrology"}],"tags":[],"updatedAt":"2026-01-13T10:02:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 10:02:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8570798","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8570798","identity":"rs-8570798","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}