Meteorology, Volume 5, Issue 2 (June 2026) – 5 articles

Seasonal precipitation forecasting remains challenging in regions with complex topography and high climatic variability, such as the state of Minas Gerais, Brazil. This study evaluates the performance of an Artificial Intelligence (AI)-based ensemble approach for seasonal precipitation prediction. The AI-based predictions are compared against outputs from multiple dynamical models, including those from the North American Multi-Model Ensemble (NMME) and the Copernicus Climate Data Store (CDS). The AI model was trained using high-resolution precipitation data from the Center for Weather Forecast and Climate Studies (CPTEC) dataset – MERGE-CPTEC – and subsequently applied to generate regional-scale seasonal forecasts. Model performance was assessed using Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Pearson Correlation (r). The results indicate that the AI-based forecasts achieve competitive performance relative to dynamical models across all seasons, exhibiting lower error metrics and improved representation of spatial precipitation patterns. The highest forecast skill was observed during winter (June-July-August, JJA), when atmospheric conditions are more stable, and precipitation variability is low. During the wet seasons (December-January-February, DJF and September-October-November, SON), despite increased convective activity and spatial heterogeneity, the AI model maintained greater spatial coherence and closer agreement with observations than the dynamical forecasts. Overall, the findings demonstrate that AI-based approaches represent a promising and computationally efficient complementary tool for regional-scale seasonal precipitation forecasting, particularly in climatically heterogeneous regions. Full article

The response of temperature extremes to recent warming at the local scale remains uncertain because changes in mean temperature may be accompanied by changes in the shape of the temperature distribution. While higher mean temperatures generally lead to more frequent heat waves and fewer cold events, variations in higher-order statistical moments can either amplify or moderate these effects. This study examines how the probability distribution of 2 m temperature has evolved during the last 80 years in Greece using the ERA-5 reanalysis dataset. The evolution of the first four statistical moments (mean, standard deviation, skewness and kurtosis) and of the 5th and 95th percentiles of daily mean temperature is calculated by splitting the time series into eight decades, with each decade representing a separate climatology. A clear increase in mean temperature is observed across Greece. However, trends in the higher-order moments are more complex: the standard deviation and skewness exhibit positive and negative trends that depend on the region and the season, while kurtosis trends are weaker with a few regional exceptions. These changes alter the response of temperature extremes to warming, resulting in non-uniform shifts of the 5th and 95th percentiles. In mountainous regions, extreme cold events during winter and autumn have decreased more strongly than expected from mean warming alone, while in marine regions extreme warm events during summer and autumn have increased beyond what would be expected by a shift in the mean. In other areas, changes in the distribution shape lead to weaker extremes than those predicted by mean warming alone. These results highlight the role that changes in temperature variability have in modulating the evolution of temperature extremes under climate warming. Full article

Puerto Rico has experienced increasingly frequent and intense extreme heat conditions in recent years, with the 2023–2024 warm seasons standing out for prolonged periods of dangerously high heat index values and widespread spatial exposure. These conditions are particularly concerning in tropical island environments, where high humidity limits physiological cooling and amplifies heat-related health risks. The main objective of this study is to identify and characterize extreme heat zones and events across Puerto Rico using NOAA-modeled heat index (apparent temperature) data, as well as to examine their spatial and temporal variability during the 2021–2024 period. Hourly modeled apparent temperature data between 2 and 4 pm, representing the warmest time of day, were analyzed for each day from June through October. Mean maximum and maximum heat index surfaces were generated for each month and warm season, and extreme heat zones were identified using the 103 °F (39.4 °C) danger threshold. Results show a persistent concentration of extreme heat in low-elevation coastal regions, particularly across the northern coastal plains from San Juan to Hatillo, with floodplain areas in Arecibo and Manatí exhibiting the highest and most consistent exposure. August was identified as the month with the highest mean maximum heat index across all study years, followed by September. The warm seasons of 2023 and 2024 exhibited the highest magnitudes and spatial extents of extreme heat, with some regions experiencing apparent temperatures exceeding 110 °F and up to 141 extreme heat days during peak afternoon hours. The findings indicate a transition from localized heat hotspots to widespread and sustained extreme heat exposure across Puerto Rico’s coastal regions. This study provides an island-scale assessment of extreme heat patterns with direct implications for public health, infrastructure planning, and heat-risk management in a warming tropical climate. Full article

Coastally Trapped disturbances (CTDs) are shifts in wind direction from the pre-dominant direction to equatorward to poleward for a period of time. These CTDs occur during the warm season off the California coast and impact coastal weather conditions and planned offshore wind plants. This study assesses the characteristics of CTD events as observed by lidar and other offshore buoys, then evaluates the ability of modeling systems to capture the correct characteristics, leveraging model output from the High-Resolution Rapid Refresh (HRRR) operational modeling system and the NOW-23 (National Offshore Wind) model dataset. CTDs were analyzed for October 2020 and May through to October of 2021, identifying 18 unique CTD events, confirmed by a nearby National Data Buoy Center (NDBC) buoy. The HRRR model captured most of these events, but the NOW-23 model output contained only 12 events. Composites of the wind, temperature, and pressure perturbations pre-, during, and post-event demonstrated the diminishment in wind speed, particularly for the alongshore component. Although the NOW-23 model captured the alongshore wind component and pressure perturbations well, the cross-shore wind component and temperature perturbations varied substantially. When the turbulent kinetic energy deviation and wind shear was positive across all levels pre-event, the NOW-23 modeling system was less likely to capture the CTD event. In contrast, the events that were captured by the model tended to have negative wind shear aloft pre-event. Full article

The Atlantic Meridional Overturning Circulation (AMOC) is a key regulator of the global climate system, yet its influence on future precipitation remains uncertain because climate models project widely varying degrees of weakening. Here, we examine the relationship between AMOC decline and global precipitation using historical and RCP8.5 simulations from ten CMIP5 models. Models are grouped by the magnitude of projected AMOC weakening, and an intermodel regression framework is used to quantify the sensitivity of precipitation to changes in overturning strength. The CMIP5 multi-model mean reproduces observed large-scale precipitation patterns. While early-century responses are modest, stronger AMOC weakening by the late century is associated with pronounced drying across the tropical North Atlantic and enhanced rainfall over the Indo-Pacific. Regression analysis indicates that precipitation within the Intertropical Convergence Zone decreases by ~2.3% per 1 Sv reduction in AMOC strength. Sensitivity experiments further show that reduced Atlantic heat transport cools the North Atlantic and shifts tropical rainfall southward. These results identify AMOC variability as an important source of uncertainty in projections of future global hydroclimate. Full article