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Abstract The African easterly jet (AEJ) and the West African Monsoon (WAM) can largely modulate high‐impact weather over Africa and the tropical Atlantic. How these features will change with a warming climate is just starting to be addressed due to global climate model limitations in resolving convection. We employ a novel regional setup for an atmospheric convection‐permitting model alongside the pseudo‐global warming (PGW) approach to address climate change impacts on the weather‐climate system of Africa during a short period of high‐impact weather. Our findings indicate that the AEJ and WAM may intensify in a future warming climate scenario. Precipitation is shown to increase over Guinea Highlands and Cameroon Mountains and shift southward due to a latitudinal expansion and increase of deep convection closer to the equator. This has relevant ramifications for the livelihood of communities that depend on water‐fed crops in tropical Africa.

Climate change has caused a more heterogeneous distribution of extreme precipitation, leading to the deterioration of drought-flood abrupt alternation (DFAA) events and soil erosion. The security and sustainable utilization of water and soil resources are severely threatened. Previous studies have focused separately on these two aspects, failing to comprehensively consider their interrelationship. Furthermore, these studies often rely on climate models with convection parameterization, resulting in substantial uncertainties. We use a 4 km convective permitting model (CPM) to generate reliable outputs for extreme precipitation. By incorporating the long-cycle drought-flood abrupt transition index and the Revised Universal Soil Loss Equation model, we analyze the changes in DFAA events and soil erosion, as well as their interconnectedness. The results show that the CPM outperforms coarse-resolution climate models in reproducing erosive rainfall and simulating the heterogeneous distribution of rainfall by capturing convection processes effectively. Projections indicate an escalation in the occurrence of DFAA events and soil erosion due to the more uneven distribution of precipitation. Specifically, the frequency of flood-to-drought (FTD) events within DFAA is projected to rise from 3.8 to 4.7 per decade. Soil loss is projected to increase 61 t·hm−2·a−1, with 73% of the area experiencing more severe soil erosion intensity. A positive correlation between FTD events and soil erosion is found throughout most of Guangdong. This correlation will be further amplified with an increase in the frequency of FTD events. Consequently, existing soil conservation measures are rendered inadequate, presenting substantial challenges for climate change adaptation and ecological protection in the region.

Understanding precipitation properties at regional scales and generating reliable future projections is crucial in providing actionable information for decision-makers, especially in regions with high vulnerability to climate change, where future changes impact ecosystem resilience, biodiversity, agriculture, water resources and human health. The South America Convection-Permitting Regional Climate Model experiment (SA-CPRCM) examines climate change effects in convection-permitting simulations at 4.5 km resolution, on climate time scales (10 year present-day and 10-year future RCP8.5 around 2100), over a domain covering most of South America, using the Met Office Unified Model (UM) convection-permitting RCM.Under the RCP8.5 scenario, precipitation in the CPRCM decreases, becomes less frequent and more seasonal over the Eastern Amazon region. Dry spells lengthen, increasing the risk of drought. In the Western Amazon, precipitation increases in the wetter austral autumn (Apr. – Jun.) and decreases in the drier austral winter and spring (July – Oct.), leading to a more distinct dry season and imposing a greater risk of contraction of the tropical forest. Over South-eastern Brazil, future precipitation increases and becomes more frequent and more intense, increasing the risk of floods and landslides. A future increase in the intensity of precipitation and extremes is evident over all these regions, regardless of whether the mean precipitation is increasing or decreasing. The CPRCM and its driving GCM respond in a similar way to the future forcing. The models produce broadly similar large-scale spatial patterns of mean precipitation and comparable changes to frequency, intensity, and extremes, although the magnitude of change varies by region and season.

The article explores the potential use of climate models to reproduce wine grape productivity at a local scale in Italy. To this end, both single and multiple regression approaches are used to link productivity data provided by two Italian wine consortia with bioclimatic indices. Temperature- and precipitation-based bioclimatic indices are computed using the observational dataset E-OBS, the high-resolution climate reanalysis product SPHERA, the regional climate model CNRM-ALADIN, and the kilometer-scale convection-permitting climate model CNRM-AROME. The multiple regression method outperforms the single regression systematically, enhancing the ability of bioclimatic indices to explain productivity variability. The results show that productivity is strongly tied to temperature-based bioclimatic indices in the area of the Consorzio per la tutela del Franciacorta in northern Italy, while for the Consorzio del Vino Nobile di Montepulciano area in central Italy both temperature- and precipitation-based indices are relevant. Climate models, providing similar results as E-OBS and SPHERA, appear to be a useful tool to explain productivity variance. In particular, the added value of convection-permitting resolution is evident when precipitation-based indices are considered. This assessment shows windows of opportunity for using climate models, especially at a convection-permitting scale, to investigate future climate change impact on wine production.

Abstract Accurately representing the precipitation diurnal cycle has long been a challenge for global climate models (GCMs). Here we evaluate the precipitation diurnal cycle in the DYAMOND global convection‐permitting models (CPMs) and CMIP6 HighResMIP models. Comparison of the high‐ (25–50 km) and low‐resolution (100–250 km) models with parameterized convection in HighResMIP shows that simply increasing model resolution does not noticeably improve the precipitation diurnal cycle. In contrast, CPMs can better capture the observed amplitude and timing of precipitation diurnal cycle. However, the simulated spatial variation of timing in CPMs is smaller than observed, leading to an exaggeration of the spatially averaged diurnal amplitude. The better‐simulated precipitation diurnal cycle in the CPMs is tied to mesoscale convective systems (MCSs), which contribute about half of the total precipitation. The observed life cycle of MCSs, including initiation and mature stages, is well captured in the CPMs, leading to a more realistic precipitation diurnal cycle.

Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models.

In this study, we utilized a high-resolution (4 km) convection-permitting Weather Research and Forecasting (WRF) simulation spanning a 13-year period (2000–2013) to investigate the climatological features of low-level jets (LLJs) over North America. The 4 km simulation enabled us to represent the effects of orography and the underlying surface on the boundary layer winds better. Focusing on the continental US and the adjacent border regions of Canada and Mexico, this study not only identified several well-known large-scale LLJs, such as the southerly Great Plains LLJ and the summer northerly California coastal LLJ, but also the winter Quebec northerly LLJ which received less focus before. All these LLJs reach their peak in the nighttime in the diurnal cycle. Thus, the different thermal and dynamic mechanisms forming these three significant LLJs are investigated in this paper. Inertial oscillation theory dominates in the Great Plain LLJ, and the California coastal LLJ is formed by the baroclinic theory, whereas the Quebec LLJ is associated with both theories. Moreover, the high-resolution simulation revealed climatic characteristics of weaker and smaller-scale LLJs or low-level wind maxima in regions with complex terrains, such as the northerly LLJs in the foothill regions of the Rocky Mountains and the Appalachians during the winter. This study provides valuable insights into the climatological features of LLJs in North America, and the high-resolution simulation offers a more detailed understanding of LLJ behavior near complex terrains and other smaller-scale features.

Previous research has suggested that the frequency and intensity of surface hazards associated with thunderstorms and convection, such as severe convective winds (SCWs), could potentially change in a future climate due to global warming. However, because of the small spatial scales associated with SCWs, they are unresolved in global climate models, and future climate projections are uncertain. Here, we evaluate the representation of SCW events in a convection-permitting climate model (Bureau of Meteorology Atmospheric Regional Projections for Australia, BARPAC-M) run over southeastern Australia for the months of December–February. We also assess changes in SCW event frequency in a projected future climate for the year 2050 and compare this with an approach based on identifying large-scale environments favourable for SCWs from a regional parent model (BARPA-R). This is done for three different types of SCW events that have been identified in this region, based on clustering of the large-scale environment. Results show that BARPAC-M representation of the extreme daily maximum wind gust distribution is improved relative to the gust distribution simulated by the regional parent model. This is due to the high spatial resolution of BARPAC-M output, as well as partly resolving strong and short-lived gusts associated with convection. However, BARPAC-M significantly overestimates the frequency of simulated SCW events, particularly in environments having steep low-level temperature lapse rates. A future decrease in SCW frequency under conditions with steep lapse rates is projected by BARPAC-M, along with less frequent favourable large-scale environments. In contrast, an increase in SCW frequency is projected under conditions of high surface moisture, with more frequent favourable large-scale environments. Therefore, overall changes in SCWs for this region remain uncertain, due to different responses between event types, combined with historical model biases.

Convection-permitting models (CPMs) are receiving growing scientific interest for their capability to accurately simulate extreme weather events at a kilometer-scale spatial resolution, offering valuable information for local climate change adaptation. This study employs both qualitative and quantitative bibliometric analysis techniques to examine research trends in CPM, utilizing data from 3508 articles published between 2000 and 2023. The annual number of publications exhibits a linear increase, rising from fewer than 50 in 2000 to over 250 after 2020, with the majority of research originating from the US, China, the UK, and Germany. The most productive institutes include the National Oceanic Atmospheric Administration (NOAA) and the National Center for Atmospheric Research (NCAR) in the US, each contributing over 10% of total publications. Title and abstract terms in publications related to keywords such as “scenario”, “climate simulation”, etc., dominate publications from 2018 to 2023, coinciding with advances in computing power. Notably, terms associated with CPM physical processes received the highest citations from 2000 to 2023, underscoring the importance of such these research topics. Given the computational expense of running CPMs and the increasing demand for future predictions using CPMs, novel methods for generating long-term simulations are imperative.

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.

In mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions. [ABSTRACT FROM AUTHOR]