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This paper surveys the current state of knowledge regarding large-scale meteorological patterns (LSMPs) associated with short-duration (less than 1 week) extreme precipitation events over North America. In contrast to teleconnections, which are typically defined based on the characteristic spatial variations of a meteorological field or on the remote circulation response to a known forcing, LSMPs are defined relative to the occurrence of a specific phenomenon—here, extreme precipitation—and with an emphasis on the synoptic scales that have a primary influence in individual events, have medium-range weather predictability, and are well-resolved in both weather and climate models. For the LSMP relationship with extreme precipitation, we consider the previous literature with respect to definitions and data, dynamical mechanisms, model representation, and climate change trends. There is considerable uncertainty in identifying extremes based on existing observational precipitation data and some limitations in analyzing the associated LSMPs in reanalysis data. Many different definitions of “extreme” are in use, making it difficult to directly compare different studies. Dynamically, several types of meteorological systems—extratropical cyclones, tropical cyclones, mesoscale convective systems, and mesohighs—and several mechanisms—fronts, atmospheric rivers, and orographic ascent—have been shown to be important aspects of extreme precipitation LSMPs. The extreme precipitation is often realized through mesoscale processes organized, enhanced, or triggered by the LSMP. Understanding of model representation, trends, and projections for LSMPs is at an early stage, although some promising analysis techniques have been identified and the LSMP perspective is useful for evaluating the model dynamics associated with extremes.

Hurricane Ian made landfall along Florida's west coast at 1905 UTC 28 September 2022 near the Fort Myers area as a high-impact storm. Here, we examine the potential link between track forecast errors near the time of landfall and errors in both the synoptic-scale upper-level flow and a shortwave moving within that flow. Five days before the actual landfall (0000 UTC 23 September), most model guidance indicated landfall would occur close to where Ian eventually came ashore. But by 0000 UTC 25 September, model forecasts were all forecasting landfall in the Florida Panhandle. One day later, the models again agreed with each other but for a landfall 100–200 km north of Tampa, Florida. By 0000 UTC 27 September, forecast models indicated landfall would occur near Tampa. Model forecasts continued shifting to the right and finally converged on Punta Gorda, Florida, as the landfall location, less than 24 h before landfall. In this short article, we hypothesize that the track of Ian depended on subtle interactions with an extratropical wave in the middle and upper atmosphere. Deterministic and ensemble model forecasts reveal that the interactions were very sensitive to the characteristics of this wave and the synoptic-scale flow in which the wave was embedded. A 1–2-dam difference in the geopotential heights played a major role in whether Ian moved north into the Panhandle or toward the east, making landfall in central Florida. Significance Statement: The purpose of this work is to look at possible causes of error in forecasts of Hurricane Ian's landfall with the intent of saving lives and reducing damage, which can run to the billions of dollars. The path that hurricanes take is strongly influenced by the larger-scale weather patterns around them. We find that small errors in the upper-level flow patterns north of the hurricane appear to have played an important role in the forecast. These errors were present in both the general flow and a wave moving through that flow, resulting in small but significant changes to the forecast path. Understanding the cause of forecast errors is a key step both in improving forecasts and in identifying types of events that are more difficult to forecast. [ABSTRACT FROM AUTHOR]

Extreme precipitation events (EPEs) are among the most pervasive weather hazards in the western Himalayan region (WHR), posing widespread damage to life, infrastructure, and agriculture. This study investigates the synoptic and large‐scale characteristics linked to winter precipitation extremes over the WHR. EPEs are identified as events surpassing the 95th percentile threshold. A composite analysis is employed using two reanalyses—the fifth‐generation European Centre for Medium‐Range Weather Forecasts Reanalysis (ERA5) and the Indian Monsoon Data Assimilation and Analysis (IMDAA)—to elucidate the synoptic conditions conducive to EPEs. Our findings suggest that EPEs in the WHR are linked to an intensified subtropical westerly jet, characteristically shifted to south than normal. Enhanced kinetic energy in the upper troposphere, attributed to increased baroclinic instability, reinforces moisture convergence and strengthens synoptic‐scale circulation, triggering deep convection and supporting EPEs. Notably, the interplay of pronounced Rossby waves sinking over the WHR and regional orography significantly modulates the intensity of western disturbances (WDs). Employing clustering analysis, we observed that the strongest EPEs are linked to anomalous vorticity in the upper to middle troposphere, together with deep convection via strengthened WDs, suggesting the potential role of large‐scale influences. Using Lagrangian method, we identify that the Arabian Sea is the primary moisture source for EPEs in the WHR. We further delved into the role of large‐scale connections and EPEs through quasi‐resonant amplification (QRA) analysis. The findings unveil distinct QRA fingerprints in meridional temperature gradients along with notably magnified, quasi‐stationary midlatitude planetary waves characterized by zonal wave numbers 6/7/8 (baroclinic waves) contributing to EPEs. Overall, our results highlight the underlying physical mechanisms for winter precipitation extremes, emphasizing QRA's role in amplifying planetary waves and promoting EPEs, underscoring the WHR's vulnerability to evolving climatic conditions. [ABSTRACT FROM AUTHOR]

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

We diagnose physical factors related to frequent compound drought and heat extremes over a Middle East and Southwest Asia (MESA; 30°–40°N, 35°–65°E) region in a recent (1999–2022) compared to a prior (1951–98) period. The recent compound extremes were related to conflict, disease transmission, and water shortages in this already semiarid region. Observed estimates and four transient climate model ensembles are used to identify the effect of El Niño–Southern Oscillation (ENSO) and atmospheric forcing by greenhouse gases and aerosols on these compound extremes in autumn (September–November), winter (December–February), spring (March–May), and summer (June–August) that may lead to practical forecast skill for future compound events. Observations and climate models indicate that MESA compound drought and heat in the autumn, winter, and spring wet seasons for the recent period were related to the La Niña phase of ENSO and an attendant northward shift of the storm track that hinders precipitation-bearing storms from moving through MESA. A comparison of different conditions in the model simulations is used to isolate the effects of La Niña and the atmospheric forcing by greenhouse gases and aerosols on compound MESA drought and heat. A comparison of recent and prior periods in the climate models, which isolates the effects of the atmospheric forcing, indicates that greenhouse gases and aerosols are related to the increases in MESA heat frequency in all seasons. A comparison of La Niña to ENSO neutral and El Niño in the recent period of the climate models indicates that La Niña is related to increases in MESA drought frequency in the wet seasons. Significance Statement: Compound drought and heat pose serious threats to the Middle East and Southwest Asia (MESA) where political and socioeconomic challenges leave its people vulnerable to climate extremes. In this region, frequent seasonal compound drought and heat in a recent (1999–2022) compared to a prior (1951–98) period were related to conflict and water shortages. Physical factors related to these compound extremes in the recent period over MESA were identified, potentially rendering future occurrences predictable. La Niña and atmospheric forcing by greenhouse gases and aerosols contributed to the compound extremes, with the former related to anomalously low precipitation in the September–November, December–February, and March–May wet seasons and the latter related to anomalous high temperatures in all seasons, including June–August. [ABSTRACT FROM AUTHOR]

North American Multi‐Model Ensemble (NMME) precipitation forecast skill over Iran is evaluated using Taylor diagrams and ranked probability skill scores (RPSS) as determined over a 29‐year test period (1991–2019). The forecast skill for both monthly (October through June for lead‐times of 0.5–3.5 months) and seasonal (October–December [OND], January–March [JFM], and April–June [AMJ] for lead‐times of 1.5–3.5 months) timescales is evaluated using six NMME models as well as multi‐model ensemble means (MMM). The latest versions of these models for forecasting Iran's precipitation have not been evaluated thus far. The Global Precipitation Climatology Center (GPCC) version 2020 dataset is used to verify the models. Among individual NMME models, Geophysical Fluid Dynamics Laboratory‐Seamless System for Prediction and Earth System Research (GFDL‐SPEAR) has generally the highest forecast skill. Both Taylor diagrams and RPSS of most of the models have indicated that the highest forecast skill is found for the month of November such that the Pearson correlation for both SPEAR and MMM is statistically significant for all lead‐times. For both monthly and seasonal timescales, the temporal Pearson correlation (TPC) between the observed and forecasts of the MMM is higher than the TPC of the individual models. The spatial Pearson correlation (SPC) and normalized centred root mean square error (NCRMSE) of the SPEAR is close to MMM, but the normalized standard deviation (NSD) of the SPEAR is closer to one compared to the MMM for months from November to March and two seasons (OND and JFM seasons). The MMM precipitation forecasts are underestimated over the northern regions and Zagros mountains for JFM and OND seasons for both 1.5‐ and 2.5‐month lead‐times. The degree to which the forecast skill of MMM is dependent on the El Niño–Southern Oscillation (ENSO) connections with precipitation over Iran is examined. Significant Spearman correlations between simultaneous observed Niño3.4 index and Iran precipitation are found for OND, but not for JFM and AMJ. The MMM reproduces the observed ENSO teleconnections to the tropical Pacific in OND, consistent with forecast skill in that season. However, the MMM also produces forecast skill in JFM and AMJ when the ENSO influence is marginal, showing that ENSO is not the only source of skill in the models. [ABSTRACT FROM AUTHOR]