Andreas Franz Prein

Over the last few years, significant progress has been made in the development of convection-permitting climate models (CPCMs), especially in improving precipitation estimates in regions with complex terrain. Recently, the South American Affinity Group (SAAG) developed a novel high-resolution dataset – hereafter referred to as the WRF-SAAG dataset – by dynamically downscaling the ERA5 reanalysis using the Weather Research and Forecasting (WRF) model over South America for the period 2000–2021. Here, we evaluate WRF-SAAG outputs across continental Chile and compare them with two widely used gridded meteorological products, CR2MET and RF-MEP, which combine reanalysis data with in situ measurements. In particular, we analyze spatial differences among climatological estimates from the three products, and we assess the quality of daily precipitation and daily maximum and minimum temperature simulations using observations from meteorological stations over the period April/2001–March/2018. We also compare the potential of these climate datasets for hydrological modeling using a conceptual, bucket-style rainfall-runoff model, evaluating the results against streamflow observations, satellite remote sensing (SRS) products, and a snow water equivalent (SWE) reanalysis product. We found that the detection accuracy varied within each Chilean macrozone, with worse performance in the Far North (17.5–26° S) and Patagonia (43.7–56° S). The evaluation of daily precipitation against station observations using Tang's Kling–Gupta efficiency (KGE _T ) reveals that all datasets performed better in reproducing precipitation in rainy regions (median KGE _T ≥ 0.65 in the Southern macrozone), while in arid areas such as the Near North during summer, the median KGE _T was negative. The CR2MET product consistently provided the best performance for extreme precipitation and temperature, partly because it includes information from the stations used for evaluation. Finally, the application of the hydrological model shows that although WRF-SAAG simulations achieved runoff estimations comparable to the best observation-based gridded products (i.e., CR2MET or RF-MEP), with the best results in the southern macrozone they provided relatively lower performance in evapotranspiration (ET) and soil moisture estimates, as well as SWE simulations of competitive quality. More broadly, the results presented here show that, despite some remaining challenges in arid climate regions, kilometer-scale climate models can deliver valuable information for hydrological applications at the daily time scale in Chile.

Forecast busts – episodes of abnormally low forecast skill – remain a persistent challenge for numerical weather prediction despite steady improvements in forecasting skill. Previous studies have highlighted the roles of moist mesoscale processes and diabatically generated potential vorticity (PV) anomalies in triggering rapid error growth and downstream circulation misrepresentation, processes whose representation is highly sensitive to model grid spacing. In this study, we select three events that were classified as forecast busts in the Integrated Forecasting System (IFS) and investigate their sensitivity to horizontal grid spacing. The cases comprise (i) the explosive cyclogenesis of Storm Dennis (February 2020), (ii) a blocking event following the extratropical transition of Hurricane Franklin (September 2023), and (iii) a June 2020 event characterized by ridge amplification due to a warm conveyor belt (WCB) over the North Atlantic. For each case, we use IFS initial conditions and perform global ICON ( Icosahedral Non-hydrostatic model) ensemble forecasts with horizontal grid spacings ranging from 40 to 2.5 km. We evaluate forecast skill against ERA5 reanalysis using anomaly correlation coefficients (ACC) of 500 hPa geopotential height. Across all cases, forecast busts are consistently linked to diabatically generated upper-level negative PV anomalies originating from strong latent heat release in organized convection, including mesoscale convective systems (MCSs), extratropical cyclones, and WCB ascent. These PV anomalies modify the upper-level waveguide, perturb the jet stream, and amplify downstream Rossby wave packets, degrading medium-range predictability. Decreasing horizontal grid spacing systematically improves the representation of diabatic processes, enhances the spatial extent and intensity of upper-level negative PV anomalies, and reduces wave-amplitude error growth. We observe that kilometer-scale simulations with horizontal grid spacing ≤5 km consistently yield the highest forecast skill, with substantial ACC improvements relative to coarser-resolution simulations. All three case studies systematically show that mesoscale diabatic processes are a primary source of forecast error in bust situations, and that kilometer-scale global simulations significantly improve the representation of scale interactions governing Rossby wave amplification. These findings underscore the importance of kilometer-scale simulations for reliably representing scale interactions in strongly diabatic flow regimes. They are not only important for weather forecasting but also for climate simulations which feature long-standing deficiencies in capturing Rossby wave breaking and associate extreme weather events.

Misrepresentation of warm-conveyor belts (WCBs) in weather forecasting models can impinge on the medium-range predictability of Rossby waves. Yet, the link between WCB errors and Rossby wave evolution remains poorly understood, despite the importance of Rossby wave breaking for weather extremes. We investigate this link using a WCB case from April 28 to May 4, 2020, which was associated with anticyclonic Rossby wave breaking over the North Atlantic and substantial ensemble variability in the forecasted Rossby wave evolution. Our analysis uses archived forecasts from the Global Ensemble Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as global simulations conducted using the Model for Prediction Across Scales (MPAS) with grid spacing from 60 km to 3.75 km. Simulations with more persistent WCBs capture Rossby wave amplification and subsequent anticyclonic wave breaking more accurately, while shorter-lived WCBs yield weaker amplification and cyclonic breaking. A systematic underestimation of the poleward extent of the 500 hPa meridional moisture flux within WCB ascent in GFS and ECMWF forecasts appears to be a key process limiting predictability. Accurate prediction of Rossby wave breaking depends not only on WCB representation but also on the upstream cyclone’s propagation and structure, mesoscale dynamics within the WCB, and upstream convection over North America. This case shows that forecasting Rossby wave breaking requires capturing the collective influence of multi-scale processes interacting with the broader Rossby wave packet. Forecast outcomes are strongly shaped by model choice, but initial conditions and horizontal resolution also play discernible roles.

Deep moist convection produces several hazards including downbursts, tornadoes, large hail, and short-duration precipitation extremes, resulting in fatalities and substantial economic losses. This study employs observational reports, lightning flashes and the ERA5 reanalysis over Spain (2015–2024) to summarize the key environmental differences between non-severe convection and each of these hazards, by ranking 203 convective parameters according to their discriminatory skill, and comparing composite vertical profiles. Summer downburst environments are markedly warmer than non-severe environments, with downdraft Convective Available Potential Energy (CAPE) emerging as the most discriminating parameter. Non-summer downbursts show enhanced wind shear relative to non-severe convection, though rarely reaching High-Shear–Low-CAPE conditions. Summer tornadoes and waterspouts display higher 0–3 km CAPE and a lower level of free convection than non-severe convection, but non-summer cases overlap substantially with non-severe environments. Hail over 5 cm in diameter occurs mostly in summer, in environments of elevated CAPE and shear, best distinguished by the WMAXSHEAR composite index. Short-duration precipitation extremes, evaluated during non-summer months, are associated with elevated moisture and some supercell signatures, supporting slow motions. These results provide a comprehensive framework for future studies on severe convective weather and climate model projections, representative of the Mediterranean severe convective environments.

Antarctic precipitation is the dominant positive term in the ice sheet surface mass balance, directly influencing its contribution to global sea level rise. In some coastal and ice-shelf regions, episodic extreme precipitation events can account for more than half of the annual snowfall total; yet while individual atmospheric phenomena such as atmospheric rivers (ARs) and cyclones are known to play important roles, their combined effects remain largely unquantified, as most existing studies focus on the impact of individual drivers. Here, we assess how atmospheric phenomena interact and shape Antarctica’s hydroclimate by applying the Multi-Object Analysis of Atmospheric Phenomena to ERA5 reanalysis over the 2001–2020 period. The algorithm simultaneously detects ARs, mid-level cyclones, anticyclones, atmospheric fronts, and upper-level jets, enabling quantification of their individual and joint contributions to mean and extreme precipitation in Antarctica within a unified framework. Our results reveal that mean and extreme precipitation are linked to broadly the same synoptic-scale systems, with cyclones showing the strongest association throughout the entire analysis domain. However, ARs play a disproportionately large role at the highest precipitation intensities, consistent with their ability to deliver concentrated moisture streams to the Antarctic coastline. Most importantly, an analysis of the 100 events with the highest peak hourly precipitation across coastal and near-coastal Antarctic regions confirms that extreme events are rarely driven by a single phenomenon acting in isolation, with the large majority involving two or more co-occurring systems. ARs, cyclones, and jet streams are consistently the most frequently co-involved phenomena, though the precise magnitude of their co-occurrence is sensitive to the spatial association method used. These findings suggest that evaluating potential changes in co-occurrence patterns among these synoptic-scale systems will be an important consideration for future projections of Antarctic precipitation extremes.

Misrepresentation of warm‐conveyor belts (WCBs) in weather forecasting models can impinge on the medium‐range predictability of Rossby waves. Yet, the link between WCB errors and Rossby wave evolution remains poorly understood, despite the importance of Rossby wave breaking (RWB) for weather extremes. We investigate this link using a WCB case from 28 April to 4 May 2020, which was associated with anticyclonic RWB over the North Atlantic and substantial ensemble variability in the forecasted Rossby wave evolution. Our analysis uses archived forecasts from the Global Ensemble Forecast System (GFS) and the European Centre for Medium‐Range Weather Forecasts (ECMWF), as well as global simulations conducted using the Model for Prediction Across Scales with grid spacing from 60 to 3.75 km. Simulations with more persistent WCBs capture Rossby wave amplification and subsequent anticyclonic wave breaking more accurately, while shorter‐lived WCBs yield weaker amplification and cyclonic breaking. A systematic underestimation of the poleward extent of the 500 hPa meridional moisture flux within WCB ascent in GFS and ECMWF forecasts appears to be a key process limiting predictability. Accurate prediction of RWB depends not only on WCB representation but also on the upstream cyclone's propagation and structure, mesoscale dynamics within the WCB, and upstream convection over North America. This case shows that forecasting RWB requires capturing the collective influence of multi‐scale processes interacting with the broader Rossby wave packet. Forecast outcomes are strongly shaped by model choice, but initial conditions and horizontal resolution also play discernible roles.

Extratropical Cyclones (ETCs) can produce high‐impact weather in mid‐latitudes, leading to extreme precipitation, winds, and storm surges. Despite the societal and economic impacts of ETCs, gaps remain in our understanding of how ETCs and their related hazards will change in a changing climate. In this study, we evaluate systematic changes in end‐of‐century ETC characteristics, including intensity, translation speed, and storm size, with a particular focus on evaluating how wind hazards related to ETCs are expected to change under future climate conditions. We leverage the 100‐member Community Earth System Model 2 Large Ensemble (LENS2) data set to identify ETCs that traverse the Northeastern U.S. Results indicate that while the annual frequency of ETCs is projected to decrease under future climate conditions, the minimum pressure decreases and maximum low‐level winds are projected to increase. We also observe track density changes in extreme future ETCs in the Northeast, with more frequent trajectories passing through the Southeastern U.S. and less zonal translation. Additionally, we leverage MPAS's variable‐resolution capabilities to downscale a subset of extreme ETC events from LENS2 to a 3‐km grid over the Northeast. Results highlight a shift toward more intense cyclones, with maximum 10 m and low‐level wind speeds increasing by 28% between present and the end of the century.

Deep moist convection produces several hazards including downbursts, tornadoes, large hail, and short-duration precipitation extremes, resulting in fatalities and substantial economic losses. This study employs observational reports, lightning flashes and the ERA5 reanalysis over Spain (2015–2024) to summarize the key environmental differences between non-severe convection and each of these hazards, by ranking 203 convective parameters according to their discriminatory skill, and comparing composite vertical profiles. Summer downburst environments are markedly warmer than non-severe environments, with downdraft Convective Available Potential Energy (CAPE) emerging as the most discriminating parameter. Non-summer downbursts show enhanced wind shear relative to non-severe convection, though rarely reaching High-Shear–Low-CAPE conditions. Summer tornadoes and waterspouts display higher 0–3 km CAPE and a lower level of free convection than non-severe convection, but non-summer cases overlap substantially with non-severe environments. Hail over 5 cm in diameter occurs mostly in summer, in environments of elevated CAPE and shear, best distinguished by the WMAXSHEAR composite index. Short-duration precipitation extremes, evaluated during non-summer months, are associated with elevated moisture and some supercell signatures, supporting slow motions. These results provide a comprehensive framework for future studies on severe convective weather and climate model projections, representative of the Mediterranean severe convective environments.

Extreme rainfall is projected to intensify with climate warming, yet changes in the spatiotemporal structure of extreme‐producing storms remain poorly understood in the tropics, as the most convectively active regions of the world are also the most data‐scarce. We here leverage the first long‐term, sub‐kilometer, sub‐hourly observational network in equatorial Singapore (730 ), with 122 rain gauges reporting 5 min intensities for 2020–2024 and 82 stations with hourly accumulations for 1980–2024, combined with X‐band radar (5 min, 100 m) to produce a high‐resolution gridded rainfall reanalysis. Our findings indicate that extreme rainfall in Singapore is becoming larger and more persistent, with intensification resulting in higher storm rainfall volumes while rainfall peak intensities remain largely unchanged. Furthermore, extreme rainfall storms are strikingly compact, with spatial and temporal correlations halving over just 2 km and 4 min, decaying faster than previously recognized and observed elsewhere. This suggests that analyses based on coarse data sets or single stations may have misrepresented rainfall intensification under warming.