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Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the "signal-tonoise paradox" and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) are widely used to assess future impacts associated with climate change at regional and local scales. RCMs must represent relevant climate variables in the present‐day climate to be considered fit‐for‐purpose for impact assessment. This condition is particularly difficult to meet over complex regions such as the Andes‐Amazon transition region, where the Andean topography and abundance of tropical rainfall regimes remain a challenge for numerical climate models. In this study, we evaluate the ability of 30 regional climate simulations (6 RCMs driven by 10 global climate models) to reproduce historical (1981–2005) rainfall climatology and temporal variability over the Andes‐Amazon transition region. We assess spatio‐temporal features such as spatial distribution of rainfall, focusing on the orographic effects over the Andes‐Amazon "rainfall hotspots" region, and seasonal and interannual precipitation variability. The Eta RCM exhibits the highest spatial correlation (up to 0.6) and accurately reproduces mean annual precipitation and orographic precipitation patterns across the region, while some other RCMs have good performances at specific locations. Most RCMs simulate a wet bias over the highlands, particularly at the eastern Andean summits, as evidenced by the 100%–2,500% overestimations of precipitation in these regions. Annual cycles are well represented by most RCMs, but peak seasons are exaggerated, especially at equatorial locations. No RCM is particularly skillful in reproducing the interannual variability patterns. Results highlight skills and weaknesses of the different regional climate simulations, and can assist in the selection of regional climate simulations for impact studies in the Andes‐Amazon transition zone. Plain Language Summary: Regional climate models (RCMs) are useful numerical tools to investigate future climate change impacts (e.g., future water availability, frequency of floods and droughts, regional warming). Regarding regional scale, RCMs are expected to perform better than global climate models due to finer spatial resolution. However, in the Andes‐Amazon transition region, assessing the performance of RCMs is challenging due to complex terrain and scarcity of observations. This region is of critical importance for the water cycle of local and regional ecological systems, but has been often overlooked in RCM assessments. Here, we evaluate how 30 regional climate simulations perform in representing precipitation regional contrasts, wet‐dry seasons, and year‐to‐year changes over the Andes‐Amazon transition region. We find that models perform differently over specific regions, with prominent overestimations at high altitudes by most RCMs. However, Eta RCM has the best performance regarding regional patterns of precipitation and its wet‐dry fluctuations. Besides overestimations during austral summer and spring, wet‐dry seasonal fluctuations are well simulated by most RCMs, but none excels in representing wet‐dry yearly fluctuations. Strengths and weaknesses of different regional climate simulations are shown, and can help choose the most appropriate simulations for distinct impact studies in this region. Key Points: Precipitation output from 30 regional climate simulations is assessed over the Andes‐Amazon in terms of climatology and temporal variabilityThe spatio‐temporal behavior of seasonality is well reproduced by most simulations, with overestimations during austral summer and springWhile orographic precipitation is a major challenge for most regional climate models, Eta satisfactorily reproduces climate patterns in the Andes‐Amazon region [ABSTRACT FROM AUTHOR]

The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the "climatic impact‐driver" (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as "physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions." We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities. Plain Language Summary: Climatic impact‐drivers (CIDs) are climate conditions that affect the things we care about in nature and society. We deepen the motivation and definitions that allowed the Intergovernmental Panel on Climate Change to identify 33 distinct CID categories including extreme heat, hydrological drought, severe wind storm, permafrost, relative sea level, marine heatwaves, and air pollution weather. Each CID category may be analyzed with specific indices that inform adaptation, mitigation and risk management. The CID Framework allows us to avoid universally labeling a climate condition as a "hazard," recognizing that the same physical condition may be detrimental for some and beneficial or inconsequential for others. This approach allows climate scientists to engage with impacts and risk experts to target specific tolerance thresholds that are system‐ and sector‐dependent. This more comprehensive description of the CID Framework provides a practical foundation for climate research, climate and impact model development, risk assessments and climate service product creation. Key Points: Deepens explanation of Climatic Impact‐Driver (CID) Framework utilized in Intergovernmental Panel on Climate Change Sixth Assessment ReportsDistinguishes practical CID types and categories that allows climate information to target conditions that affect the things we care aboutNeutral Framework does not pre‐judge beneficial, detrimental or neutral outcomes which are system‐ and sector‐dependent [ABSTRACT FROM AUTHOR]

Based on a series of experiments conducted by two regional climate models (RCA4 and LMDZ) with and without soil moisture-atmosphere coupling, we investigate the role of soil moisture on the occurrence of surface air temperature extremes and its persistence in Southeastern South America. Our analysis reveals that both factors, soil moisture-atmosphere coupling and relatively drier soil conditions, enhance the temperature extremes. In addition, the existence of soil-atmosphere coupling and the associated soil moisture variability is crucial for the development of the extremes in SESA. The key role of soil-atmosphere coupling is also reflected in the intrinsic persistence of hot days, which is greater in simulations with interactive soil moisture than in those with prescribed soil conditions. In the absence of soil-atmosphere coupling, the imprint of the anomalous dry (and also wet) soil conditions on the intensity and persistence of hot days is weaker. [ABSTRACT FROM AUTHOR]

Interannual variability of surface air temperature over South America is investigated and, based on previous studies, thought to be partly the consequence of soil–atmosphere interaction. Annual and monthly averages of surface air temperature, evapotranspiration, heat fluxes, surface radiation and cloud cover, simulated by two regional climate models, RCA4 and LMDZ, were analyzed. To fully reveal the role of soil as a driver of temperature variability, simulations were performed with and without soil moisture-atmosphere coupling (Control and Uncoupled). Zones of large variance in air temperature and strong soil moisture-atmosphere coupling are found in parts of La Plata Basin and in eastern Brazil. The two models show different behaviors in terms of coupling magnitude and its geographical distribution, being the coupling strength higher in RCA4 and weaker in LMDZ. RCA4 also shows greater amplitude of the annual cycle of the monthly surface air temperature compared to LMDZ. In both regions and for both models, the Uncoupled experiment tends to be colder and exhibits smaller amplitude of the interannual variability and larger evaporative fraction than the Control does. It is evidenced that variability of the land surface affects, and is affected by, variability of the surface energy balance and that interannual temperature variability is partly driven by land–atmosphere interaction. [ABSTRACT FROM AUTHOR]