Highlights

⛰️Some local-scale phenomena such as sea breezes and mountain wind systems can not be well represented by the resolution of most climate models.

🌆The difference in observed warming trends between cities and their surroundings can partly be attributed to urbanization. Future urbanization will amplify the projected air temperature change in cities regardless of the characteristics of the background climate.

😕Statistical methods are improving to downscale global climate models to more accurately depict local or regional projections.

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Distilling regional climate information from multiple lines of evidence and taking the user context into account will increase the fitness, usefulness and relevance for decision-making and enhances the trust users will have in applying it. This distillation process can draw upon multiple observational datasets, ensembles of different model types, process understanding, expert judgement and indigenous knowledge. Important elements of distillation include attribution studies, the characterization of possible outcomes associated with internal variability and a comprehensive assessment of observational, model and forcing uncertainties and possible contradictions using different analysis methods. Taking the values of the relevant actors into account when co-producing climate information, and translating this information into the broader user context, improves the usefulness and uptake of this information.

Observations and Models as Sources of Regional Information

The use of multiple sources of observations and tailored diagnostics to evaluate climate model performance increases trust in future projections of regional climate. The availability of multiple observational records, including reanalyses, that are fit for evaluating the phenomena of interest and account for observational uncertainty, are fundamental for both understanding past regional climate change and assessing climate model performance at regional scales. Employing tailored, process-oriented and potentially multivariate diagnostics to evaluate whether a climate model realistically simulates relevant aspects of present-day regional climate increases trust in future projections of these aspects.

Currently, scarcity and reduced availability of adequate observations increase the uncertainty of long term temperature and precipitation estimates (virtually certain). Precipitation measurements in mountainous areas, especially of solid precipitation, are strongly affected by gauge location and setup. Over data-scarce regions or over complex orography, gridded temperature and precipitation products are strongly affected by interpolation methods. Lack of access to the raw station data used to create gridded products compromises the trustworthiness of these products since the influence of the gridding process on the product cannot be assessed. The use of statistical homogenization methods reduces uncertainties related to long-term warming estimates at regional scales (virtually certain).

Regional reanalyses provide surrogates of observed climate variables that are highly relevant in areas with scarce surface observations. Regional reanalyses represent the distributions of precipitation, surface air temperature, and surface wind, including the frequency of extremes, better than global reanalyses. However, their usefulness is limited by their short length, the typical regional model errors, and the relatively simple data assimilation algorithms.

Global and regional climate models are important sources of climate information at the regional scale. Global models by themselves provide a useful line of evidence for the construction of regional climate information through the attribution or projection of forced changes or the quantification of the role of the internal variability. Dynamical downscaling using regional climate models adds value in representing many regional weather and climate phenomena, especially over regions of complex orography or with heterogeneous surface characteristics. Increasing climate model resolution improves some aspects of model performance. Some local-scale phenomena such as land sea breezes and mountain wind systems can only be realistically represented by simulations at a resolution of the order of 10 km or finer. Simulations at kilometre-scale resolution add value in particular to the representation of convection, sub-daily precipitation extremes (high confidence) and soil moisture precipitation feedbacks. Sensitivity experiments aid the understanding of regional processes and can provide additional user-relevant information.

The performance of global and regional climate models and their fitness for future projections depend on their representation of relevant processes, forcings and drivers and on the specific context. Improving global model performance for regional scales is fundamental for increasing their usefulness as regional information sources. It is also key for improving the boundary conditions for dynamical downscaling and the input for statistical approaches, in particular when regional climate change is strongly influenced by large-scale circulation changes. Increasing resolution per se does not solve all performance limitations. Including the relevant forcings (e.g., aerosols, land-use change and stratospheric ozone concentrations) and representing the relevant feedbacks (e.g., snow–albedo, soil-moisture–temperature, soil moisture–precipitation) in global and regional models is a prerequisite for reproducing historical regional trends and ensuring fitness for future projections. The sign of projected regional changes of variables such as precipitation and wind speed is in some cases only simulated in a trustworthy manner if 32 relevant regional processes are represented.

Statistical downscaling, bias adjustment and weather generators are useful approaches for improving the representation of regional climate from dynamical climate models. Statistical downscaling methods with carefully chosen predictors and an appropriate model structure for a given application realistically represent many statistical aspects of present-day daily temperature and precipitation. Bias adjustment has proven beneficial as an interface between climate model projections and impact modelling in many different contexts. Weather generators realistically simulate many statistical characteristics of present-day daily temperature and precipitation, such as extreme temperatures and wet- and dry-day transition probabilities.

The performance of statistical downscaling, bias adjustment and weather generators in climate change applications depends on the specific model and on the dynamical climate model driving it. Knowledge is still limited about suitable predictors for statistical downscaling of regional climate change, particularly for precipitation. Bias adjustment cannot overcome all consequences of unresolved or strongly misrepresented physical processes, such as large-scale circulation biases or local feedbacks, and may instead introduce other biases and implausible climate change signals. Using bias adjustment as a method for statistical downscaling, particularly for coarse-resolution global models, may lead to substantial misrepresentations of regional climate and climate change. Instead, dynamical downscaling may resolve relevant local processes prior to bias adjustment, thereby improving the representation of regional changes. The performance of statistical approaches and their fitness for future projections depends on predictors and change factors taken from the driving dynamical models.

Different types of climate model ensembles allow for the assessment of regional climate projection uncertainties, although ensemble spread is not a full measure of the uncertainty. Multi-model ensembles enable the assessment of regional climate response uncertainty. Discarding models that fundamentally misrepresent processes relevant for a given purpose improves the fitness of multi-model ensembles for generating regional climate information. At the regional scale, multi-model mean and ensemble spread are not sufficient to characterize low-probability, high-impact changes or situations where different models simulate substantially different or even opposing changes. In such cases, storylines aid the interpretation of projection uncertainties. Since AR5, the availability of multiple single-model initial-condition large ensembles (SMILEs) allows for a more robust separation of model uncertainty and internal variability in regional-scale projections and provides a more comprehensive spectrum of possible changes associated with internal variability.

Interplay between Human Influence and Internal Variability at Regional Scales

Human influence has been a major driver of regional mean temperature change since 1950 in many sub-continental regions of the world (virtually certain). Regional-scale detection and attribution studies as well as observed emergence analysis provide robust evidence supporting the dominant contribution of human influence to regional temperature changes over multidecadal periods.

While human influence has contributed to multi-decadal mean precipitation changes in several regions, internal variability can delay emergence of the anthropogenic signal in long-term precipitation changes in many land regions. Multiple attribution approaches, including optimal fingerprinting, grid-point detection, pattern recognition and dynamical adjustment methods, as well as multi-model, single-forcing large ensembles and multi-centennial paleoclimate records, support the contribution of human influence to several regional multi-decadal mean precipitation changes. At regional scale, internal variability is stronger and uncertainties in observations, models and human influence are all larger than at the global scale, precluding a robust assessment of the relative contributions of greenhouse gases, stratospheric ozone, different aerosol species and land use/land cover changes. Multiple lines of evidence, combining multi-model ensemble global projections with those coming from SMILEs, show that internal variability is largely contributing to the delayed or absent emergence of the anthropogenic signal in long-term regional mean precipitation changes.

Various mechanisms operating at different time scales can modify the amplitude of the regional-scale response of temperature, and both the amplitude and sign of the response of precipitation, to human influence. These mechanisms include non-linear temperature, precipitation and soil moisture feedbacks, slow and fast responses of sea surface temperature patterns and atmospheric circulation changes to increasing greenhouse gases.

Urban Climate

Many types of urban parameterizations simulate radiation and energy exchanges in a realistic way. For urban climate studies focusing on the interplay between the urban heat island and regional climate change, a simple single-layer parameterization is fit for purpose. New networks of monitoring stations in urban areas provide key information to enhance the understanding of urban microclimates and improve urban parameterizations.

The difference in observed warming trends between cities and their surroundings can partly be attributed to urbanization. Annual-mean daily minimum temperature is more affected by urbanization than annual-mean daily maximum temperature. The global annual-mean surface air temperature response to urbanization is, however, negligible.

Future urbanization will amplify the projected air temperature change in cities regardless of the characteristics of the background climate, resulting in a warming signal on minimum temperatures that could be as large as the global warming signal. A large effect is expected from the combination of future urban development and more frequent occurrence of extreme climatic events, such as heatwaves.

Distillation of Regional Climate Information

The process of distilling regional climate information from multiple lines of evidence can vary substantially from one case to another. Although methodologies for distillation have been established, the process is in practice conditioned by the sources available, the actors involved and the context, which depend heavily on the regions considered, and framed by the question being addressed. To make the most appropriate decisions and responses to changing climate, it is necessary to consider all physically plausible outcomes from multiple lines of evidence, especially in the case when they are contrasting.

Confidence in the distilled regional climate information is enhanced when there is agreement across multiple lines of evidence. For example, the apparent contradiction between the observed decrease in Indian monsoon rainfall over the second half of the 20th century and the projected long-term increase is explained by attribution of the trends to different forcings, with aerosols dominating recently and greenhouse gases in the future. For the Mediterranean region, the agreement between different lines of evidence, such as observations, projections by regional and global models, and understanding of the underlying mechanisms, provides high confidence in summer warming that exceeds the global average.

The outcome of distilling regional climate information can be limited by inconsistent or contradictory information. Initial observational analyses of the Cape Town drying showed a strong, post-1979 association between increasing greenhouse gases, changes in a key mode of variability (the Southern Annular Mode) and drought in the Cape Town region. However, not all global models show this association, and subsequent analysis extending farther back in time, when human influence was weaker, showed no strong association in observations between the Southern Annular Mode and Cape Town drought. Thus, despite the consistency among global-model future projections, there is medium confidence in a projected future drier climate for Cape Town. Likewise, the distillation process results in low confidence in the influence of Arctic warming on mid-latitude climate because of contrasting lines of evidence.