The GloSAT project is developing a new observational analysis of global air temperature change over land and ocean since the late 18^th century.

A new global analysis processing system has been developed that uses a computationally efficient spatial statistical method to estimate air temperature anomaly fields from historical observations. This will be the first presentation of this analysis approach. This method, based on Gaussian Markov Random Fields, jointly estimates temperature anomaly fields over land and ocean based on weather station and ship-based air temperature observations. The increased computational efficiency of the approach compared to conventional kriging-based estimates allows for increased spatial resolution in the analysis.

Observational uncertainties are represented within the analysis framework to propagate uncertainty into the output ensemble data set. This accounts for errors arising from uncorrelated effects and structured errors such as residual biases in observations from an individual weather station or ship after correction. Observational error models have been co-developed with project partners providing the input land and marine data products.

Initial results from the application of the analysis system to GloSAT air temperature observation data will be demonstrated.

How to cite: Morice, C., Berry, D., Cornes, R., Cowtan, K., Cropper, T., Kennedy, J., Kent, E., Rayner, N., Steptoe, H., Osborn, T., Taylor, M., Wallis, E., and Winn, J.: A new observational analysis of near surface air temperature change since the late 18th century developed for the GloSAT project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17030, https://doi.org/10.5194/egusphere-egu24-17030, 2024.

How to cite: Faulkner, A., Cornes, R., Chan, S., Siddons, J., and Kent, E.: Pushing the time and space resolution for historical marine data: new datasets of sea-surface temperature and marine air temperature, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16365, https://doi.org/10.5194/egusphere-egu24-16365, 2024.

Arctic winter climate is rapidly changing, with more variable snow depths, spring snowmelt timing, and more frequent midwinter thaw events. Less predictable conditions disrupt ecosystem balances and development in Arctic communities, and understanding winter variability across the Arctic and its influence on climate the whole year is needed to mitigate consequences of changing winters. However, access to in situ measured data has been extremely limited and scattered in local databases. Hence, cross-Arctic winter studies are few and based on remotely sensed data with larger spatial and temporal coverage, but less local sensitivity, and the winter contribution to annual average temperature change has not been investigated across the Arctic.

In this project, which runs January 2024-December 2025, we 1) obtain, clean and standardize in situ soil surface temperature, snow depth and soil moisture data from climate monitoring programs across the Arctic and create a unique database with cross-Arctic in situ winter climate data from the last appr. 30 years. We will use this dataset to 2a) estimate the accuracy of remotely sensed soil surface temperature, snow depth and soil moisture data using the regression model with the best fit, and quantify the bias, for each major Arctic region. We further 2b) construct an open access Winter Variability Index (WVI) for each major Arctic region based on the winter phenomena (average snow depth, snowmelt date, frequency of winter thaw events) that are most important drivers of a clustering analysis such as hierarchical clustering or autoencoders. Finally, we 3) use the change in WVI and in annual mean temperatures for each decade in a function-on-function regression analysis, which will quantify the contribution of winter variability change to annual average temperature changes in each Arctic region.  

The project will produce a comprehensive dataset with potential for further research and will improve our region-specific understanding of remotely sensed data accuracy, and the WVI allows scientists or local communities to classify Arctic winter data within a quantitative framework of pan-Arctic winter variability also in the future, and to understand how important changes in winter variability is for Arctic climate changes the whole year.

How to cite: Rasmussen, L. H., Markussen, B., and Ditlevsen, S.: When winter is weird: Quantifying the change in winters across the Arctic, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5669, https://doi.org/10.5194/egusphere-egu24-5669, 2024.

Over the last years destructive climate events like heat-waves and floods have been attributed to global warming. The warming trend is higher in the Arctic than the global average, therefore its contribution to the global warming is relatively larger than other areas.  In addition, the temperature increase in Greenland is an important driver of the melting of the Greenland Ice-Sheet, which leads to sea level rise. It is crucial to have temperature records of high quality in this area to properly assess the climatic changes. This will improve understanding of the involved physical mechanisms, past changes and improve predictions for the future temperature development in the Arctic. This study investigates daily averages of the sub-daily 2m air temperature measurements from the DMI Greenland station network spanning 1958-present day. The data from before 1958 has only been digitized as monthly averages, where parts of the data has been homogenized. The data from after 1958 has not been homogenized. The data is currently used for assessment and predictions of the surface mass balance of the Greenland Ice Sheet, temperature/climate reanalyses, global temperature products, validation of proxy data, etc.

This study assesses the errors due to uneven sampling times, and presents a method to improve the calculation of daily average temperatures. The current practice is to average the available measurements without considering what time of day they are from and how the measurements are distributed. In addition to missing values, the weather station network has periods of different measurement practices for different stations. Before automatic weather stations were introduced several weather stations have periods with measurements only from the daytime, when the temperatures are generally higher. This can lead to a warm bias in the daily averages compared to more recent data where there are generally observations for every hour of the day. This can affect temperature trends, as the positive bias is generally found in earlier periods of the dataset. As the diurnal cycle varies over the year, the magnitude of the bias also varies seasonally. The correction method used to reduce these biases is a moving average over the years with evenly distributed data covering the whole day, taking into consideration hours of observation present and time of year of the station in question. The biases before and after correction are assessed. The largest average simulated bias before correction over several years of data is 1.25 °C. The largest average simulated bias after correction is reduced to 0.45 °C. These corrections will improve the monthly and annual average temperatures for the DMI Greenland station network, as they are calculated from the daily average temperatures. This study is limited to the weather station network in Greenland managed by DMI, but the findings are relevant for other networks in other areas, as long as there are uneven sampling times and a diurnal temperature cycle. This problem might affect decision making on a high level, like assessing a breach of the Paris agreement. 

How to cite: Rapp, D., Høyer, J. L., and Vinther, B. M.: Sampling errors in daily average temperatures from Greenland climate records, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19073, https://doi.org/10.5194/egusphere-egu24-19073, 2024.

When comparing model output to historical radiosonde observations, it is typically assumed that the radiosonde has ascended directly above its launch site and has not been moved by the wind. The introduction of Global Navigation Satellite System (GNSS) receivers on radiosondes in the late 1990s has led to a recent change in the availability of balloon trajectory data. However, this information was still not always transmitted, despite being the basis for estimating wind. Radiosondes can drift a few hundred kilometres, especially in mid-latitudes during winter months, depending on conditions and time of year. Position errors may result in significant representation errors when assimilating corresponding observations.We have developed a methodology to calculate changes in balloon position during vertical ascent using limited information, such as the vertical wind profile in historical observation reports. The investigation analysed the method's sensitivity to various parameters, including the vertical resolution of the input data, the assumption about the vertical ascent speed of the balloon, and the departure of the Earth's surface from a sphere. To validate the method, modern GNSS sonde data reports were considered, which provided the full trajectory of the balloon and the estimated wind. The evaluation was conducted by comparing the results with ERA5 and conducting low-resolution data assimilation experiments. The study evaluated the accuracy of reconstructing the trajectory of radiosonde using original data of varying vertical resolution. The results indicate that the accuracy of the reconstructed trajectory can be improved by using more accurate balloon positions, which reduces both representation and systematic errors.Radiosonde measurements have a wide range of applications, including near-real-time use by forecasters and Numerical Weather Prediction (NWP), as well as for air pollution and other scientific investigations, such as climate monitoring. The production of climate reanalyses that directly assimilate radiosonde observations, such as ERA5, is expected to benefit from more accurate historical balloon position data, similar to NWP. 

How to cite: Voggenberger, U., Haimberger, L., Amgrogi, F., and Poli, P.: Balloon drift estimation and improved position estimates for radiosondes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1995, https://doi.org/10.5194/egusphere-egu24-1995, 2024.

In preparation for the next generation Copernicus reanalysis ERA6, we aim at providing an as complete as possible global insitu upper-air dataset, augmented with additional data and metadata that allow to reduce observation and representation errors in those data. 

We first reduce representation errors using actual launch times and balloon positions (see presentation by Voggenberger et al.). This allows to get more smaller observation - background (obs-bg) departures compared to the original obs-bg departures calculated during assimilation with ERA5.

The obs-bg departures form the basis for comprehensive statistics-based adjustment of biases in temperature, wind direction and also humidity, using the RAOBCORE/RICH method. The corresponding software has been further improved compared to the past year, now including also adjustments for mobile platforms and paying attention to adjustments of the most recent parts of the time series. 

Results from bias-adjusted temperature records indicate realistic spatial trend heterogeneity and very good fit to reprocessed satellite data products, clearly better than what could be achieved in preparation to the present operational reanalysis ERA5. Temperature Background departures from ERA5 increase substantially, both in terms of mean and standard deviations when going back to the early 1950s and 1940s. It is tried to shed some light whether this increase comes from poorer quality observations or from issues arising due to the less strongly observationally constrained ERA5 state during this period.

Wind direction adjustments are necessary only at a few stations but also have a clearly positive effect on trend heterogeneity and obs-bg departures. Humidity bias adjustments are more delicate, since it is not sufficient to shift the distributions by a mean value, rather one has to adjust also the shape of the distributions. Results from humidity bias adjustments, considered experimental, are quite promising up to 300 hPa. Pervasive strong drying trends over large countries like the US and China could be substantially reduced. More detailed verification is needed, however.

How to cite: Haimberger, L., Ambrogi, F., and Voggenberger, U.: Bias adjustments for the global historical radiosonde network in preparation for ERA6, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12929, https://doi.org/10.5194/egusphere-egu24-12929, 2024.

In a previous work (Parey et al. 2010, [1]) a link had been identified between the trends in temperature mean and variance in Europe, with geographical and seasonal differences, and the climate models at the time performed poorly in representing the observed link. In this study, we update the results for summer temperature and go further in looking for possible causes of the observed differences between France and Spain. Then, the performance of the last CMIP6 models in representing the link is assessed in order to anticipate how this link could evolve in the future.

More precisely, in summer in France the trends in mean and variance are generally positively correlated: variance increases when mean increases, whereas in Spain the reverse behavior is generally found. This behavior has been confirmed with recent observation data: individual timeseries as well as the gridded EOBS database. We show that this can be explained by the fact that summers in Spain are more constantly hot, and the variance is explained by the occurrence of milder spells, while in France, summers are mild and the variance is explained by the occurrence of hot spells. The evolution of the link throughout the 1950-2022 period has been studied and revealed that parts of the south of France recently turned toward a similar behavior as Spain, with a decrease in variance when mean increases. The investigated question then is the following: will French summers resemble Spanish summers in the future? To answer the question, the ability of CMIP6 models to represent the observed link is first assessed, and then, the evolution projected by the best performing ones toward the end of the century is studied.

Better understanding the evolution of this link is important to anticipate future hot temperature extremes in France in the long term for adaptation. Owing to the larger impact of variance compared to mean on the intensity of the extremes, this question is crucial for the anticipation of future hot extremes. Do we need to anticipate a general hot summer climate in the second half of the century, or will the summers become warmer with more frequent heat waves and possible large deviations to very hot temperatures?

Reference:

[1] Parey S., Dacunha-Castelle D., Hoang T.T.H. : Mean and variance evolutions of the hot and cold temperatures in Europe; Clim Dyn (2010) 34:345–359, DOI 10.1007/s00382-009-0557-0

How to cite: Parey, S., Lemaire, C., and Hoang, T. T. H.: The link between temperature mean and variance trends in summer in France and Spain and its evolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4100, https://doi.org/10.5194/egusphere-egu24-4100, 2024.

The identification of non-stationary behavior in extremes is crucial for the analysis of climatic and environmental data. As an alternative to classical extreme value theory, the investigation of events that break new records proves particularly appealing. While the increment in averaged temperatures over recent decades is well-characterized and studied, the characterization of changes in record-breaking temperature events remains an open problem. In this work, the R package RecordTest (Castillo-Mateo et al. 2023a), available on CRAN (https://CRAN.R-project.org/package=RecordTest) and GitHub (https://github.com/JorgeCastilloMateo/RecordTest), is introduced. This package offers a framework for non-parametric analysis of non-stationary behavior in extremes, based on record-breaking analysis. The main idea of these inference tools is based on verifying whether the observed records in the data align with the distribution of record occurrences under stationary series of random variables. Several hypothesis tests are proposed to detect trends or change-points in record occurrences based on both upper and lower records, both in the forward and backward series. The package also implements all necessary steps in such analyses, including data preparation, record identification, exploratory tools, and supplementary graphical tools. The tools included in the package are introduced together with a careful analysis of the impact of climate change on the occurrence of calendar day records in 36 series available from the ECA&D of daily maximum temperatures in the Iberian Peninsula from 1960 to 2021 (Castillo-Mateo et al. 2023b). The objective also includes characterizing the record occurrences in different periods of the year and in different spatial regions. The effects of climate change are heterogeneous within the year, being autumn the season where the effects are weaker and summer where they are stronger. Concerning the spatial variability, the Mediterranean and the North Atlantic region are the areas where the effects are more and less clear, respectively.

References:

Castillo-Mateo, J., Cebrián, A. C., and Asín, J.: RecordTest: An R package to analyse non-stationarity in the extremes based on record-breaking events, Journal of Statistical Software, 106(5), 1-28, https://doi.org/10.18637/jss.v106.i05, 2023a.

Castillo-Mateo, J., Cebrián, A. C., and Asín, J.: Statistical analysis of extreme and record-breaking daily maximum temperatures in peninsular Spain during 1960-2021, Atmospheric Research, 293, 106934, https://doi.org/10.1016/j.atmosres.2023.106934, 2023b.

How to cite: Castillo-Mateo, J., Cebrián, A. C., and Asín, J.: Analyzing non-stationarity in record-breaking temperature events over Peninsular Spain in 1960-2021 using the R package RecordTest, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4028, https://doi.org/10.5194/egusphere-egu24-4028, 2024.

Recent findings showed that extreme events such as daily maximum temperature record-breaking are not following a stationary pattern, with trends associated to global warming [1]. But there are spatial variability identifying the change-point when this pattern is dated and, most interesting, there is not evidence of non-stationarity in North stations of Spain in spring and autumn. To this regard, it is essential to develop novel tools and models that represent the seasonal and spatial variability, and effectively capture the spatio-temporal dependence of covariates of interest. Effective detection will enable us to more accurately describe and predict which regions may be affected by stronger trends.
In this framework, the utility of spatio-temporal models including relevant covariates for the occurrence of records is evident. However, achieving this objective requires the preliminary identification of the covariates and interaction terms that influence the occurrence of records. Thus, we propose a two-level generalized linear models (GLM) approach to detect spatio-temporal dependence of covariates of interest in daily maximum temperature record-breaking. To do so, we took daily maximum temperatures in the 1960-2022 period in 36 stations distributed over the peninsular Spain with low level of missing values (below 0.5%). We computed the calendar day record-breaking by binarizing the temporal series assigning a one only if a particular daily maximum on year ‘t’ is above all its previous years on the same date [2]. First, for each station a local logistic regression was applied setting the trend term as log(t-1); note that the trend term in the probability of a record, on the logit scale, is -log(t-1) under a stationary climate. Finally, all the estimated beta coefficients for the trend were gathered and correlated with spatial covariates such as latitude, longitude, altitude, and distance to the coast. We found that only log-altitude and log-distance showed a significant positive correlation with the trend coefficients, being the latter the one with a higher effect (r=0.59). Although preliminary, these results showed a straightforward approach to model the relationship between spatial covariates and the temporal trend in extreme events, in particular, record of maximum temperatures. In addition, we anticipate that this tool will be potentially useful to build models based on atmospheric covariates.

References:
[1] Castillo-Mateo, J., Cebrián, A. C., and Asín, J. (2023). Statistical analysis of extreme and record-breaking daily maximum temperatures in peninsular Spain during 1960–2021. Atmospheric Research, 106934. https://doi.org/10.1016/j.atmosres.2023.106934
[2] Castillo-Mateo, J., Cebrián, A. C., and Asín, J. (2023). RecordTest: An R Package to Analyze Non-Stationarity in the Extremes Based on Record-Breaking Events. Journal of Statistical Software, 106, 1-28. https://doi.org/10.18637/jss.v106.i05

How to cite: Gracia-Tabuenca, Z., Barrio-Torres, E., Castillo-Mateo, J., Asin, J., Cebrian, A. C., and Abaurrea, J.: Two-level GLM approach for detection of spatio-temporal interactions in daily maximum temperature record-breaking in Spain 1960-2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7095, https://doi.org/10.5194/egusphere-egu24-7095, 2024.

Near-surface wind speed over the Tibetan Plateau exerts profound impacts on many environmental issues, while long-term (> 50 years) trend and multidecadal variability in the wind speed and its underlying causes in the Tibetan Plateau remain largely unknown. Here, by examining homogenized wind speed data from 104 meteorological stations over the Tibetan Plateau for 1961-2020 and reanalysis datasets, we investigate the variability and long-term trend in the near-surface wind speed and reveal the role played by the westerly and Asian monsoon interaction. The results show that the homogenized wind speed declined ( -0.091 m s^-1 decade^-1, p < 0.05) annually, with the strongest trend in spring ( -0.131 m s^-1 dec^-1, p < 0.05), and the weakest trend in autumn (-0.071 m s^-1 dec^-1, p < 0.05). However, there is a distinct multidecadal variability of wind speed, which manifested in an abrupt increase in wind speed in 1961-1970, a sustained decrease in 1970-2002, and a consistent increase since 2002. The observed variations in NSWS over different studied periods are likely linked to interdecadal variations in atmospheric-ocean interactions, and the correlation analysis unveiled a more important role of background westerly and East Asian winter monsoon in modulating near-surface wind changes over the Tibetan Plateau when compared to East Asian summer monsoon and Indian summer monsoon. The potential physical processes associated with westerly and Asian monsoon changes are further examined, in terms of: (i) regional pressure gradient force (i.e., geostrophic wind speed); (ii) vertical thermal momentum transfer (i.e., atmospheric stratification thermal instability); (iii) vertical dynamic momentum transfer (i.e., vertical wind shear); and (iv) Tibet Plateau Vortices (TPVs). They all partly concord with wind change, which demonstrates, that to varying degrees, these processes are possible causes of near-surface wind speed changes over the Tibetan Plateau. 

How to cite: Zhang, G., Azorin-Molina, C., A. Guijarro, J., Minola, L., and Ma, Y.: Variability and  trends of near-surface wind speed over the Tibetan Plateau: the role played by the westerly and Asian monsoon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9727, https://doi.org/10.5194/egusphere-egu24-9727, 2024.

The observed decline and reversal in average near-surface wind speeds over recent decades have been widely reported and confirmed globally and regionally (especially in mid-latitude areas). The trends in extreme wind speeds do not completely synchronize with average wind speeds, and the variability of extreme wind speeds and their driving mechanisms are still unclear, especially for the high mountainous regions, such as the Tibetan Plateau. This study utilizes a homogenized dataset of daily maximum wind speeds from 1973 to 2020 in the Tibetan Plateau to investigate the variability of daily maximum wind speed and uncover the physical processes through which atmospheric circulation influences it. The results indicate that: (1) the daily maximum wind speed in the Tibetan Plateau has significantly decreased in most areas from 1973 to 2020, with the largest decreasing trends in magnitude observed in the spring(-0.57 m s^-1dec^-1,p<0.05), summer (-0.46 m s^-1dec^-1, p<0.05), winter (-0.41 m s^-1dec^-1,p<0.05), and autumn (-0.37 m s^-1dec^-1, p<0.05).  The frequency of daily maximum wind speed exceeding 95% quantile shows a similar pattern. (2) Large-scale atmospheric circulation plays a key role in influencing the changes in daily maximum wind speed, with the westerly and monsoon patterns explaining 35%~57% of daily maximum wind speed variations. (3) The physical processes associated with atmospheric circulation changes such as geostrophic wind(0 to -0.4 m s^-1 dec ^-1), anticyclone activity(0 to -0.2 K dec ^-1), vertical wind shear(0 to -0.1 m s^-1 dec ^-1), and Tibetan Plateau low vortex(-0.69 to 0.26 dec^-1) across the Tibetan Plateau region, partly explain the decreasing trends in magnitude and frequency of daily maximum wind speed. Our study provides some new insights for the management of sand and dust storms as well as the utilization of wind energy resources in the Tibetan Plateau.

Keywords: Tibetan Plateau, daily maximum wind speed, trend, atmospheric circulation, physical processes

How to cite: Ma, Y., Zhang, G., Wang, Y., and Ma, H.: A secular decline in daily maximum wind speed over the Tibetan Plateau from 1973-2020 and its possible causes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7382, https://doi.org/10.5194/egusphere-egu24-7382, 2024.

Climate change is very evident and transformative in the 21^st century. Variations in rainfall and temperature define the ecosystem services, extreme events (flood, drought, heatwave etc.) and agricultural activities, especially in developing countries such as India where the majority of the population is agricultural dependent. Apart from global variability in rainfall and temperature, a significant change has been also observed at the regional level. Thus it’s also very important to study regional variation in climate extreme indices. In this paper historical variation (1991-2022) in rainfall and temperature extreme indices has been computed over the Gandhinagar district, Gujarat. Daily rainfall and temperature gridded data from the Indian Meteorological Department (IMD) have been acquired and used for the Expert Team on Climate Change Detection and Indices (ETCCDI) extreme indices calculation. A data homogenization technique was applied for the quality control and outliers were removed. As recommended by ETCCDI core rainfall (CDD, CWD, PRCPTOT, R30mm) and temperature (CSDI, WSDI, DTR) extreme indices were calculated. The trend was calculated using Mann-Kendall (M-K) and Sen’s slope estimator. It was observed that Consecutive Dry Days (CDD) are decreasing (R² = 0.05) as the days having a minimum rainfall of 10mm are increasing (R² = 0.02). Cold Spell Duration Indicator (CSDI) suggests that the period having minimum temperature is decreasing (R² = 0.3) which is also supported by the decreasing Diurnal Temperature Range (DTR) (R² = 0.25). The observed change in CSDI and DTR is more influenced by the minimum rather than maximum temperature as a continuous rise in minimum temperature has been observed since 1991. Since Gandhinagar district is developing at a very rapid pace the results of this study could be used for the climate change policy framework and better sustainable developmental strategies for the region.

How to cite: Kumar, S. and Pathak, B.: Observed climate extreme indices trends and variation in Gandhinagar, Gujarat, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3369, https://doi.org/10.5194/egusphere-egu24-3369, 2024.

Indus River Basin supports approximately 268 million inhabitants, covering four different countries, including Pakistan, India, China and Afghanistan. Most countries situated in this basin are highly vulnerable to the impacts of climate change, even though their emissions are low. The basin has a large agriculture-based economy; therefore, we are interested in assessing the impacts of climate change on agriculture under best- and worst-case scenarios. In this study, a mean ensemble of four CMIP6 climate models was used with a resolution of 50km, and the analysis was conducted on a subbasin scale through delineation of the entire Indus basin. The climate indices estimated are extreme maximum temperature (TXx), extreme minimum temperature (TNn), heat stress (TR), maximum 5-day precipitation (RX5day), 95^th percentile of precipitation (R95pTot), consecutive dry days (CDD), growing season length (GSL), and heat sum (HS). The climate indices data was analyzed spatially and temporally by estimating trends, their significance, areal mean time series and plotting Hovmuller diagrams. Temperature-based indices TXx and TNn show a significant increase across the basin, while TR seems to increase mostly in the Lower Indus Basin plain. This may lead to crop failure due to excess heat and put more pressure on available water resources. Precipitation-based indices RX5day and R95pTot show a rise in flood risks in the eastern subbasins, while the number of CDD will vary across the region. The Hovmuller diagrams show that spatial precipitation patterns will be irregular across the basin, making it difficult to follow traditional agricultural practices. A significant increase in GSL and HS is noted in the Upper Indus Basin, making the region more suitable for agriculture, and the seasonal differences plot showed that the summer months of July, August & September will have the largest increase in extreme precipitation with high spatial variability. To conclude, climate adaptation measures are necessary, and a nexus-based resource management approach should be considered in the decision-making process. 

How to cite: Pyarali, K., Zhang, L., and Al-Qubati, A.: Unveiling Tomorrow's Climate: Indus River Basin in Focus - A Comprehensive Assessment using Cutting-edge CMIP6 Data for SSP126 and SSP585 Scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18428, https://doi.org/10.5194/egusphere-egu24-18428, 2024.