About every 10 years, the Norwegian Centre for Climate Services publishes a national climate assessment report, presenting the updated historical climate change and climate projections towards the end of this century. This paper documents the model experiment used to generate high-resolution climate and hydrological projections for the new climate assessment report published in October 2025. The model experiment follows the standard modelling chain for hydrological impact assessment, i.e., climate model selection - downscaling and bias adjustment - hydrological modelling. However, compared with the model experiment for the climate assessment report published in 2015, all modelling components have been improved in terms of data availability, data quality and methodology. Specifically, a large climate model ensemble was available and new criteria were developed to select tailored climate projections for Norway. Two bias-adjustment methods (one univariate and one multivariate) were applied to account for the uncertainty of method choice. The hydrological modelling was improved by implementing a physically-based Penman-Monteith method for evaporation and a glacier model accounting for glacier retreat under climate change scenarios. Besides model description, this paper elaborates the effects of different bias-adjustment methods and the contribution of climate models and bias-adjustment methods to the uncertainty of climate and hydrological projections under the RCP4.5 scenario as examples. The results show that the two bias-adjustment methods can contribute larger uncertainty to seasonal projections than climate models. The multivariate bias-adjustment method improves hydrological simulations, especially in the reference period, but cannot conserve climate change signals of the original climate projections. The dataset generated by the presented modelling chain provides the most updated, comprehensive and detailed hydrometeorological projections for mainland Norway, serving as a knowledge base for climate change adaptation to decision makers at various administrative levels in Norway.

This study presents the first multi-model ensemble evaluation of the added value of km-scale (2-4 km) Convection-Permitting climate Models (CPM) for representing urban temperatures and Urban Heat Islands (UHI) across multiple cities, compared to coarser-resolution (12 km) Regional Climate Models (RCM). To this end, we analyze 21 CPM simulations driven by the ERA-Interim reanalysis from 2000 to 2009. The ensemble spans six families of RCMs with urban parameterizations of varying complexity, ranging from simple surface/bulk schemes with modified surface parameters, to dedicated Urban Canopy Models (UCM). We evaluate the potential added value of the increased horizontal resolution and improved urban representation of the CPMs compared to their respective driving RCMs. This evaluation is performed across six European cities where data from urban and rural meteorological stations are available. The study focuses on how well the models represent daily minimum and maximum temperatures, as well as UHI intensities, in winter and summer. The results show that all models incorporating an urban parameterization capture some urban signal; however, their performance varies from city to city. CPMs with dedicated UCMs or simpler bulk schemes that represent key processes (e.g., anthropogenic heat in winter or solar radiation trapping in summer) can better simulate UHIs. The added value for temperatures outside urban areas is limited, as RCMs already match observations well. However, it can be significant over cities when the increased resolution is combined with the adoption of a more complex urban parameterization in the CPM. While all urban CPMs in the ensemble can represent average UHIs, even the best ones struggle to capture extreme UHI intensities.

Estimates, based on a simple formula that approximates the probability of heavy 24-hr rainfall, give surprisingly high correlations with the observed number of days with more than 20 mm precipitation from rain gauge data around the world. A background for this formula is presented, which includes a series of past projects EU-SPECS, KlimaDigital, CORDEX FPS southeast Africa, and EU-SPRINGS. Although the initial analysis focussed on Norway, subsequent work has shown that this simple formula provides a reasonable description of the frequencies of heavy rainfall recorded by rain gauge data from all around the world where data has been available. Furthermore, a comparison with intensity-duration-frequency (IDF) analysis in Norway suggests that there is a fractal dependency between temporal scales that may be utilised in a simple IDF formula, and one question is whether this framework works elsewhere as it is related to the said formula for 24-hr precipitation. Even if such simple models are not sufficiently accurate for estimating design values, they may nevertheless be useful for benchmarking because they are easy to apply and require very little computational resources. The IDF framework has been tested on results from convection permitting regional climate model simulations to assess whether the fractal scaling varies in space. It may also provide a framework for downscaling future heavy precipitation statistics, both in terms of dynamical as well as empirical-statistical downscaling. A demonstration of both the formula for heavy 24-hr precipitation as well as for IDFs are incorporated in a test app for monitoring precipitation https://ocdp.met.no.

Convection-permitting regional climate models (CPRCMs) have demonstrated enhanced capability in capturing extreme precipitation compared to regional climate models (RCMs) with convection parameterization schemes. Despite this, a comprehensive understanding of their added values in terms of daily or hourly extremes, especially at the local scale, remains limited. In this study, we conduct a thorough comparison of daily and hourly extreme precipitation from the HARMONIE Climate (HCLIM) model at 3 km resolution (HCLIM3) and 12 km resolution (HCLIM12) across Norway's diverse landscape, divided into five regions, using both gridded and in situ observations. Our main focus is on investigating the added value of CPRCMs (i.e., HCLIM3) compared to RCMs (i.e., HCLIM12) for extreme precipitation from regional to local scales and on quantifying to what extent CPRCMs can reproduce the orographic effect on extreme precipitation at both daily and hourly scales. We find that HCLIM3 matches observations better than HCLIM12 for daily and hourly extreme precipitation across most grid points in Norway, while HCLIM12 underestimates the extremes, especially for hourly extremes. At the regional scale, HCLIM3 captures the maximum 1 d precipitation (RX1 d) and the maximum 1 h precipitation (Rx1 h) more accurately across most regions and seasons, with some exceptions. Specifically, for daily extremes, it shows larger summer biases in the east, south and west, as well as return levels biases in the east; for hourly extremes, larger biases are observed in the summer and in the west compared to HCLIM12. Besides this, for the local scale, HCLIM3 also outperforms HCLIM12 in most regions and seasons, except for a slightly larger summer bias in terms of daily extremes in the south and west. Overall, HCLIM3 consistently demonstrates added value in simulating daily extremes in the middle and northern regions at both regional and local scales, as well as in simulating hourly extremes at all 10 stations, compared with HCLIM12. Both HCLIM3 and HCLIM12 capture the seasonality of daily extremes well, while HCLIM3 performs better for the hourly extremes, accurately representing their frequency and intensity. Additionally, both models capture the reverse orographic effect of Rx1 h at the regional scale, with no added value seen in HCLIM3, while, at the local scale, HCLIM3 shows added value compared to HCLIM12 in representing the reverse orographic effect of Rx1 d in all seasons except summer. This study highlights the importance of more realistic CPRCMs in providing reliable insights into the characteristics of precipitation extremes across Norway's five regions. Such information is crucial for effective adaptation management to mitigate severe hydro-meteorological hazards, especially for the local extremes.

High-resolution climate information is critical for the Vulnerability, Impacts, Adaptation, and Climate Services (VIACS) communities. Coordinated ensembles generated by initiatives like the Coordinated Regional Climate Downscaling Experiment (CORDEX) provide consistent and comparable information for the present and future over all land areas of the globe. This manuscript focuses on the European CORDEX initiative (EURO-CORDEX) and its coordinated effort to build regional climate ensembles for the years to come. In its first phase, EURO-CORDEX produced a rich ensemble of regional climate simulations under different representative concentration pathway scenarios. The EURO-CORDEX dataset is openly available and was fed into the Regional Atlas of the IPCC Sixth Assessment Report. However, this ensemble suffered from several shortcomings, which the community seeks to address in the next phase of production. Chief among these is the oft-cited criticism that the selection of GCMs that provide input to the regional climate models was not rigorous and that the resulting ensemble represents an "ensemble of opportunity." The present paper provides a description of how the community has addressed these shortcomings. We present a comprehensive, flexible, and traceable evaluation framework and toolkit for assessing the suitability of GCMs for downscaling, using EURO-CORDEX as an example. Its value lies in its explicit recognition of subjectivity and mechanisms implemented to transparently track decision-making. Further, the utility of the framework extends well beyond predownscaling decisions to also include postdownscaling investigations performed by the VIACS communities and beyond, to include researchers investigating such topics as model biases, future constraints, and exploring future storylines. SIGNIFICANCE STATEMENT: The European Coordinated Regional Climate Downscaling Experiment initiative (EURO-CORDEX) community has created a comprehensive evaluation framework and open-source toolkit for global climate model assessment. These approaches provide a robust, transparent, and traceable approach to both pre-and postdownscaling ensemble designs whose utility extends well beyond the immediate regional climate community. This will enable improved and more nuanced assessments of regional climate change and its impacts.

AbstractAs global temperatures continue to rise, the impact of heatwaves becomes increasingly striking. The increasing frequency and intensity of these events underscore the critical need to understand regional‐scale mechanisms and feedback, exacerbating or mitigating heatwave magnitude. Here, we use an ensemble of convection‐permitting regional climate models (CPRCMs) to elucidate future heatwave changes at fine spatial scales. We explore whether the recently highlighted drier/warmer signal introduced by CPRCMs improves summer temperature extremes representation and if it modulates future heatwave changes compared to convection‐parameterizing regional climate models (RCMs). In historical runs, CPRCMs show a more realistic representation of summer maximum temperature especially on a ground‐station‐based evaluation. CPRCMs project substantially drier conditions than RCMs. This is associated with a modulation of heatwave temperature changes which show diversified spatial patterns, magnitudes, and signs. CPRCMs ensemble shows an overall reduction in heatwave metrics future changes inter‐model spread compared to the RCMs ensemble.

We present an interactive climate atlas providing visualisations of future regional climate projections of temperature and precipitation in northern Europe from multiple sources. It is based on results of both empirical-statistical and dynamical downscaling of multi-model ensembles from CMIP5 and CMIP6 including several emission scenarios. Displayed alongside each other, the projected climate change estimated from different model ensembles can be compared and contrasted. The comparison can be useful to evaluate the robustness of the climate change information and the influence of methodological choices such as the downscaling method and the selection of global climate models, and to explore how the level of greenhouse gas emissions may affect the future climate. The application is developed by researchers at the Norwegian Meteorological Institute and is freely available at the website futureclimate.met.no/dse4KSS.

If the shape of mathematical curves describing local weather statistics are systematically influenced by large-scale conditions and geographical factors, then it may be possible to downscale this kind of information directly. Such curves may include probability density functions (pdfs) for daily temperature/precipitation or intensity-duration-frequency (IDF) curves for estimating return values of intense sub-daily rainfall. Downscaling the shape of such curves may be referred to as ‘downscaling climate’ if we regard ‘local climate’ as the statistical description of various weather parameters. This approach is distinct from the more traditional approach ‘downscaling weather’, where one seeks to estimate particular local states for instance on a day-by-day basis. We present work on downscaling the shapes of pdfs and IDFs involving large multi-model ensembles for the application in climate change adaptation efforts. Our efforts also include an evaluation of both methodology and the global climate models' (GCMs) ability to reproduce observed large-scale climatic variability in terms of the salient spatio-temporal covariance structure. We emphasise that it’s important to combine different strategies for downscaling, e.g. regional climate models (RCMs) and empirical-statistical downscaling (ESD) that are based on different assumptions, for getting robust future regional climate projections.

We used empirical–statistical downscaling to derive local statistics for 24 h and sub-daily precipitation over the Nordic countries, based on large-scale information provided by global climate models. The local statistics included probabilities for heavy precipitation and intensity–duration–frequency (IDF) curves for sub-daily rainfall. The downscaling was based on estimating key parameters defining the shape of mathematical curves describing probabilities and return values, namely the annual wet-day frequency, fw, and the wet-day mean precipitation, μ. Both parameters were used as predictands representing local precipitation statistics as well as predictors representing large-scale conditions. We used multi-model ensembles of global climate model (CMIP6) simulations, calibrated on the ERA5 reanalysis, to derive local projections and future outlooks. Our analysis included an evaluation of how well the global climate models reproduced the predictors in addition to assessing the quality of downscaled precipitation statistics. The evaluation suggested that present global climate models capture essential aspects of the covariance, and there was a good match between annual wet-day frequency and wet-day mean precipitation derived from ERA5 on the one hand and local rain gauges in the Nordic region on the other. Furthermore, the ensemble downscaled results for annual fw and μ were approximately normally distributed, which may justify using the ensemble mean and standard deviation to describe the ensemble spread. Hence, our efforts provide a demonstration for how empirical–statistical downscaling can be used to provide practical information on heavy rainfall, which subsequently may be used for impact studies. Future projections for the Nordic region indicated little increase in precipitation due to more wet days, but most of the contribution comes from increased mean intensity. The west coast of Norway had the highest probabilities of receiving more than 30 mm d−1 precipitation, but the strongest relative trend in this probability was projected over northern Finland. Furthermore, the highest estimates for trends in 10-year and 25-year return values were projected over western Norway, where they were high from the outset. Our results also suggested that future precipitation intensity is sensitive to future emissions, whereas the wet-day frequency is less sensitive.

We show that the fraction of Earth’s surface area receiving daily precipitation is closely connected to the global statistics of local wet-day frequency and mean precipitation intensity. Our analysis, based on the ERA5 reanalysis, revealed a close match between the global mean surface temperature and both the total mass of 24-h precipitation falling on Earth’s surface as well as surface area receiving 24-h precipitation in the ERA5 data, highlighting the dependency between the greenhouse effect and the global hydrological cycle. Moreover, the total planetary precipitation and the global daily precipitation area represent links between the global warming and extreme precipitation amounts that traditionally have not been included in sets of essential climate indicators. Hence, both the total amount of precipitation falling on Earth’s surface and the fraction of the surface area on which it falls represent two key global climate indicators for Earth’s global hydrological cycle. Furthermore, the global surface area fraction of daily precipitation is connected with the global statistics of local wet-day frequencies in addition to the mean precipitation intensity. Previous work suggest that these two parameters can be used to get approximate estimates of probability for heavy daily precipitation amounts. Based on these results, we argue that the present set of global climate indicators should be extended to include the total global daily precipitation and the fraction of Earth's surface area receiving daily precipitation. It's also useful to compute the fractional global surface area with daily precipitation exceeding thresholds such as 30 mm/day and 50 mm/day.

Both the total amount of precipitation falling on Earth’s surface and the fraction of the surface area on which it falls represent two key global climate indicators for Earth’s global hydrological cycle. We show that the fraction of Earth’s surface area receiving daily precipitation is closely connected to the global statistics of local wet-day frequency and mean precipitation intensity, based on the ERA5 reanalysis. Our analysis of the global statistical distribution of local temporal mean precipitation intensity μ revealed a close link between (1) its global spatial average ⟨μ⟩ and (2) the total daily precipitation falling on Earth’s surface divided by the global surface area fraction on which it falls. This correlation highlights an important connection, since the wet-day frequency and the mean precipitation intensity represent two key parameters that may be used to approximately infer the probability of heavy rainfall on local scales. We also found a close match between the global mean surface temperature and both the total mass of 24-h precipitation falling on Earth’s surface as well as surface area receiving 24-h precipitation in the ERA5 data, highlighting the dependency between the greenhouse effect and the global hydrological cycle. Moreover, the total planetary precipitation and the daily precipitation area represent links between the global warming and extreme precipitation amounts that traditionally have not been included in sets of essential climate indicators. A simple back-of-the-envelope calculation suggests that half of Δ⟨μ⟩ /Δ T = 0.47 mm/day can be explained by increased 24-h precipitation and half by a reduced fractional area of 24-h precipitation.

We analyzed the evolution of extreme annual surface air temperature and rainfall on Earth, based on the recurrence rate of record-breaking events, and found the highest recurrence rates for record-high annual temperatures in the tropics, as opposed to the polar regions with the fastest warming. Both recurrence rates and the global surface area fraction with daily mean surface air temperatures exceeding 30° and 40°C provide further evidence for extremely hot years becoming more common and widespread. A similar analysis for precipitation highlighted some regions with more record-high annual total precipitation and others with record-low annual precipitation typically associated with drought. A multimodel ensemble of 306 runs with global climate models [Coupled Model Intercomparison Project phase 6 (CMIP) Shared Socioeconomic Pathways 2-45 (SSP2-45)] reproduced the statistics of record-breaking high temperatures, but there were some differences for the reanalysis precipitation record-breaking recurrence rates. The global climate model simulations suggested a slightly altered geographical pattern for record-breaking annual precipitation recurrence rates.

Abstract Taking advantage of a large ensemble of Convection Permitting‐Regional Climate Models on a pan‐Alpine domain and of an object‐oriented dedicated analysis, this study aims to investigate future changes in high‐impact fall Mediterranean Heavy Precipitation Events at high warming levels. We identify a robust multi‐model agreement for an increased frequency from central Italy to the northern Balkans combined with a substantial extension of the affected areas, for a dominant influence of the driving Global Climate Models for projecting changes in the frequency, and for an increase in intensity, area, volume and severity over the French Mediterranean. However, large quantitative uncertainties persist despite the use of convection‐permitting models, with no clear agreement in frequency changes over southeastern France and a large range of plausible changes in events' properties, including for the most intense events. Model diversity and international coordination are still needed to provide policy‐relevant climate information regarding precipitation extremes.

Estimated solar irradiances from CAMS, PVGIS SARAH-2, Solargis, Meteonorm, PVGIS ERA5, and NASA POWER are benchmarked against measurements conducted at 34 ground stations in Norway at latitudes between 58 and 76 degrees N. degrees N. We find that the data products that mainly rely on high-resolution, geostationary satellite images, i.e., CAMS, PVGIS SARAH-2, and Solargis, have higher accuracy with lower relative Mean Absolute Error (rMAE) and relative Mean Bias Error. By dividing the stations in distinct categories, such as above 65 degrees N, degrees N, snow-affected and horizon-shaded, challenges with irradiance estimation that are common in Norway and at high latitudes in general are highlighted and discussed. The accuracy of the data products is dependent on latitude, and by excluding stations above 65 degrees N, degrees N, the median rMAE of the different data products improves 3.2 - 9.4 % abs compared to the median rMAE when including all stations, depending on data product. Similarly, by excluding snow- affected stations, the median rMAE improves 1.9 - 8.1 % abs , depending on data product. The improvement in rMAE by excluding snow-affected stations is partially related to the difficulty of separating snow on the ground from cloud cover in satellite images. This difficulty is illustrated by concrete examples of irradiance time series from clear sky days when the ground is covered in snow. Although the performance of the data products is dependent on the categorization of stations, i.e., latitude, snow conditions, and local topography, the relative performance between the products is maintained regardless of sub-division.

We conducted an analysis of hydrological cycle variations across 13 regions of varying sizes distributed across different continents. The analysis is based on five reanalysis datasets of daily precipitation, all produced by the European Centre on Medium-Range Weather Forecasts (ECMWF): ERA5 high-resolution, ERA5 ensemble, CERA-20C, ERA20-C and ERA20-CM. We examined several climate indicators, including the daily mean precipitation, the 75th and 99th percentiles, the precipitation area fraction and the area fractions with precipitations exceeding 10 and 20 mm. We evaluated the ability of the reanalyses to capture precipitation at specific spatial scales using scale-separation diagnostics based on 2D wavelet decomposition. The climatological energy spectra of precipitation derived from the analysis describe the scales that each reanalysis can accurately reproduce, serving as a unique signature for each dataset. We compared the spatial scales that were comparable across the different reanalyses and examined the temporal trends of energy on those scales. The results indicate that the hydrological cycle is undergoing changes in all regions, with some variations observed across different regions. Common features include an increase in intense precipitation events and a decrease in the corresponding spatial extent. The ensemble of ERA5 reanalyses exhibited the smallest effective resolution, as determined by the scale-separation method, and displayed more pronounced trends compared to other reanalyses. Notably, an acceleration of changes is evident in the last 20 years. However, Central Asia may be an exception, showing relatively less noticeable changes in the hydrological cycle. The study analysed hydrological cycle variations across 13 regions using five reanalysis datasets from ECMWF. Climate indicators like precipitation mean, percentiles and area fractions were examined. The analysis revealed changes in the hydrological cycle globally, including increased intense precipitation events and decreased spatial extent. The ensemble of ERA5 reanalyses showed the smallest effective resolution and displayed pronounced trends, while Central Asia exhibited relatively fewer noticeable changes. image

To implement the geographical and future climate adaptation of building moisture design for building projects, practitioners need efficient tools, such as precalculated climate indices to assess climate loads. Among them, the Frost Decay Exposure Index (FDEI) describes the risk of freezing damage for clay bricks in facades. Previously, the FDEI has been calculated for 12 locations in Norway using 1961–1990 measurements. The purpose of this study is both updating the FDEI values with new climate data and future scenarios and assessing how such indices may be suitable as a climate adaptation tool in building moisture safety design. The validity of FDEI as an expression of frost decay potential is outside the scope of this study. Historical data from the last normal period as well as future estimated climate data based on 10 different climate models forced by two emission scenarios (representative concentration pathways 4.5 and 8.5) have been analyzed. The results indicate an overall decline in FDEI values on average, due to increased winter temperatures leading to fewer freezing events. Further, the variability between climate models and scenarios necessitates explicit uncertainty evaluations, as single climate model calculations may result in misleading conclusions due to high variability between models.

Extreme precipitation events lead to dramatic impacts on society and the situation will worsen under climate change. Decision-makers need reliable estimates of future changes as a basis for effective adaptation strategies, but projections at local scale from regional climate models (RCMs) are highly uncertain. Here we exploit the km-scale convection-permitting multi-model (CPM) ensemble, generated within the FPS Convection project, to provide new understanding of the changes in local precipitation extremes and related uncertainties over the greater Alpine region. The CPM ensemble shows a stronger increase in the fractional contribution from extreme events than the driving RCM ensemble during the summer, when convection dominates. We find that the CPM ensemble substantially reduces the model uncertainties and their contribution to the total uncertainties by more than 50%. We conclude that the more realistic representation of local dynamical processes in the CPMs provides more reliable local estimates of change, which are essential for policymakers to plan adaptation measures.

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