We diagnose physical factors related to frequent compound drought and heat extremes over a Middle East and Southwest Asia (MESA; 30 degrees-40 degrees N, 35 degrees-65 degrees 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 Nifio-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 Nifia 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 Nifia and the at-mospheric 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 Nifia to ENSO neu-tral and El Nifio in the recent period of the climate models indicates that La Nifia 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 Nifia 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 tem-peratures in all seasons, including June-August.

Of concern to Colorado River management, as operating guidelines post-2026 are being considered, is whether water resource recovery from low flows during 2000-20 is possible. Here, we analyze new simulations from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to determine plausible climate impacts on Colorado River flows for 2026-50 when revised guidelines would operate. We constrain projected flows for Lees Ferry, the gauge through which 85% of the river flow passes, using its estimated sensitivity to meteorological variability together with CMIP6-projected precipitation and temperature changes. The critical importance of precipitation, especially its natural variability, is emphasized. Model projections indicate increased precipitation in the upper Colorado River basin due to climate change, which alone increases river flows by 5%-7% (relative to a 2000-20 climatology). Depending on the river's temperature sensitivity, this wet signal compensates for some, if not all, of the depleting effects of basin warming. Considerable internal decadal precipitation variability (;5% of the climatological mean) is demonstrated, driving a greater range of plausible Colorado River flow changes for 2026-50 than previously surmised from treatment of temperature impacts alone: the overall precipitation-induced Lees Ferry flow changes span from -25% to 140%, contrasting with a range from -30% to -5% from expected warming effects only. Consequently, extreme low and high flows are more likely. Lees Ferry flow projections, conditioned on initial drought states akin to 2000-20, reveal substantial recovery odds for water resources, albeit with elevated risks of even further flow declines than in recent decades.

Characteristics and physical features related to low precipitation across many years in Southern Africa that lead to societal disruptions are diagnosed using observed analyses and an ensemble of historical coupled climate model simulations during 1921 to 2014. Four regions are evaluated, as identified through a hierarchical clustering algorithm applied to the Standardized Precipitation Index (SPI) during the October–April precipitation season. Although dryness spanning many October–April occurs periodically in each region, they seldom occur simultaneously, consistent with largely insignificant SPI cross‐correlations between them. However, characteristics relevant to low precipitation across many years are generalizable between the four regions, including the serial persistence of October–April precipitation, the likelihood of consecutive dry October–April, and the likelihood of dry October–April in temporal extents of up to 10 consecutive such 7‐month seasons. Systematic precipitation persistence is not a feature in any of the four Southern Africa regions, as serial correlations of October–April SPI are not statistically significant at any time lags. It follows that there is an exponential‐folding decay in the likelihood of consecutive October–April for various SPI thresholds and that there is a large spread in the likelihood of low October–April SPI across many years. In terms of physical features, low October–April SPI in each Southern Africa region is closely related to local atmospheric circulations; however, they are not as closely related to sea surface temperatures (SSTs). These results suggest that dryness spanning many years is determined primarily by persistent local circulations related to atmospheric variability and to a lesser extent variability related to SST anomalies, including the El Niño–Southern Oscillation.

A cooling trend in summer (May-August) daytime temperatures since the mid-twentieth century over the central United States contrasts with strong warming of the western and eastern United States. Prior studies based on data through 1999 suggested that this so-called warming hole arose mainly from internal climate variability and thus would likely disappear. Yet it has prevailed for two more decades, despite accelerating global warming, compelling reexamination of causes that in addition to natural variability could include anthropogenic aerosol-induced cooling, hydrologic cycle intensification by greenhouse gas increases, and land use change impacts. Here we present evidence for the critical importance of hydrologic cycle change resulting from ocean-atmosphere drivers. Observational analysis reveals that the warming hole's persistence is consistent with unusually high summertime rainfall over the region during the first decades of the twenty-first century. Comparative analysis of large ensembles from four different climate models demonstrates that rainfall trends since the mid-twentieth century as large as observed can arise (although with low probability) via internal atmospheric variability alone, which induce warming-hole-like patterns over the central United States. In addition, atmosphere-only model experiments reveal that observed sea surface temperature changes since the mid-twentieth century have also favored central U.S cool/wet conditions during the early twenty-first century. We argue that this latter effect is symptomatic of external radiative forcing influences, which, via constraints on ocean warming patterns, have likewise contributed to persistence of the U.S. warming hole in roughly equal proportion to contributions by internal variability. These results have important ramifications for attribution of extreme events and predicting risks of record-breaking heat waves in the region.

Hawaii's recent drought is among the most severe on record. Wet-season (November-April) rainfall deficits during 2010-19 rank second lowest among consecutive 10-yr periods since 1900. Various lines of empirical and model evidence indicate a principal natural atmospheric cause for the low rainfall, mostly unrelated to either internal oceanic variability or external forcing. Empirical analysis reveals that traditional factors have favored wetness rather than drought in recent decades, including a cold phase of the Pacific decadal oscillation in sea surface temperatures (SSTs) and a weakened Aleutian low in atmospheric circulation. But correlations of Hawaiian rainfall with patterns of Pacific sea level pressure and SSTs that explained a majority of its variability during the twentieth century collapsed in the twenty-first century. Atmospheric model simulations indicate a forced decadal signal (2010-19 vs 1981-2000) of Aleutian low weakening, consistent with recent observed North Pacific circulation. However, model ensemble means do not generate reduced Hawaiian rainfall, indicating that neither oceanic boundary forcing nor a weakened Aleutian low caused recent low Hawaiian rainfall. Additional atmospheric model experiments explored the role of anthropogenic forcing. These reveal a strong sensitivity of Hawaiian rainfall to details of long-term SST change patterns. Under an assumption that anthropogenic forcing drives zonally uniform SST warming, Hawaiian rainfall declines, with a range of 3%-9% among three models. Under an assumption that anthropogenic forcing also increases the equatorial Pacific zonal SST gradient, Hawaiian rainfall increases 2%-6%. Large spread among ensemble members indicates that no forced signals are detectable.

We identify physical factors leading to extreme wet winters over the contiguous U.S. and examine whether preconditions operated during winter 2019 (December 2018 to February 2019) when record precipitation occurred that led to billion-dollar flood disasters along the Missouri and Mississippi Rivers. Models and observations are used to determine the effect of slow-varying forcing that may lead to practical forecast skill for extreme wet winters. Atmospheric models indicate that sea surface temperatures during strong eastern Pacific El Niño events like 1983 and 1998 can drive extreme wet winters over the contiguous U.S. These strong El Niños shift the distribution of contiguous U.S. precipitation to wetter conditions with a mean wetting of 1.5–2.0 standard deviations of the interannual variability. The shift to wetter conditions leads to a fivefold increase in the probability of wet winters of the magnitude observed in 2019. On longer timescales, observations indicate contiguous U.S. winter precipitation has increased over the last century. Analysis of historical coupled model simulations indicate anthropogenically-forced shifts to wetter conditions over the last century of 0.2–0.4 standard deviations of the interannual variability. While increasing the risk of extreme wet winters like 2019, this effect is a limited source of predictability during any particular winter. Concerning 2019 specifically, preconditioning factors of the risk for extreme contiguous U.S. winter wetness were weak or absent and offered little practical early warning. The ongoing central Pacific El Niño that winter did not significantly alter the risk of the wetness, and thus the extreme 2019 conditions are judged not to have been a seasonal forecast of opportunity.

Characteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

Observed United States trends in the annual maximum 1-day precipitation (RX1day) over the last century consist of 15%–25% increases over the eastern United States (East) and 10% decreases over the far western United States (West). This heterogeneous trend pattern departs from comparatively uniform observed increases in precipitable water over the contiguous United States. Here we use an event attribution framework involving parallel sets of global atmospheric model experiments with and without climate change drivers to explain this spatially diverse pattern of extreme daily precipitation trends. We find that RX1day events in our model ensembles respond to observed historical climate change forcing differently across the United States with 5%–10% intensity increases over the East but no appreciable change over the West. This spatially diverse forced signal is broadly similar among three models used, and is positively correlated with the observed trend pattern. Our analysis of model and observations indicates the lack of appreciable RX1day signals over the West is likely due to dynamical effects of climate change forcing—via a wintertime atmospheric circulation anomaly that suppresses vertical motion over the West—largely cancelling thermodynamic effects of increased water vapor availability. The large magnitude of eastern U.S. RX1day increases is unlikely a symptom of a regional heightened sensitivity to climate change forcing. Instead, our ensemble simulations reveal considerable variability in RX1day trend magnitudes arising from internal atmospheric processes alone, and we argue that the remarkable observed increases over the East has most likely resulted from a superposition of strong internal variability with a moderate climate change signal. Implications for future changes in U.S. extreme daily precipitation are discussed.

Whether distinct wintertime U.S. climate conditions exist for central-Pacific (CP) versus eastern-Pacific (EP) El Niño events is explored using atmospheric and coupled ocean–atmospheric models. Results using the former agree with most prior studies indicating different U.S. temperature and precipitation patterns associated with El Niño flavors. Causes are traced to equatorial rainfall sensitivity to both magnitudes and spatial patterns of sea surface temperatures (SSTs) distinguishing CP and EP cases. Warmer east equatorial Pacific Ocean SSTs during EP than CP events, specifically for strong EP cases, are responsible for greater east equatorial Pacific rainfall, which displaces tropospheric circulation anomalies eastward over the Pacific–North American region. Weak-amplitude EP cases and all CP events since 1980 fail to excite east equatorial Pacific rainfall, thus not initiating the dynamical chain of effects characterizing strong EP cases. Over the contiguous United States, the difference in tropospheric circulations between strong EP and CP events describes a cyclonic pattern that renders the former colder and wetter. Regional signals include notably colder western and warmer eastern U.S. surface temperatures during EP versus CP events, and higher southwestern and southeastern U.S. precipitation during EP events. We demonstrate the important result—new to studies of observed El Niño flavor impacts—that coupled models largely reproduce the sensitivities of atmospheric models. Confirmed hereby is the realism of prior estimates of El Niño flavor impacts that relied on atmospheric models alone. We further examine predictability of El Niño flavors using coupled forecasts, demonstrating that SST distinctions between CP and EP events and their diverse U.S. wintertime impacts are predictable at least a season in advance.

The Facility for Weather and Climate Assessments (FACTS) developed at the NOAA Physical Sciences Laboratory is a freely available resource that provides the science community with analysis tools; multimodel, multiforcing climate model ensembles; and observational/reanalysis datasets for addressing a wide class of problems on weather and climate variability and its causes. In this paper, an overview of the datasets, the visualization capabilities, and data dissemination techniques of FACTS is presented. In addition, two examples are given that show the use of the interactive analysis and visualization feature of FACTS to explore questions related to climate variability and trends. Furthermore, we provide examples from published studies that have used data downloaded from FACTS to illustrate the types of research that can be pursued with its unique collection of datasets.

The 2017 flash drought arrived without early warning and devastated the U.S. northern Great Plains region comprising Montana, North Dakota, and South Dakota and the adjacent Canadian Prairies. The drought led to agricultural production losses exceeding $2.6 billion in the United States, widespread wildfires, poor air quality, damaged ecosystems, and degraded mental health. These effects motivated a multiagency collaboration among academic, tribal, state, and federal partners to evaluate drought early warning systems, coordination efforts, communication, and management practices with the goal of improving resilience and response to future droughts. This essay provides an overview on the causes, predictability, and historical context of the drought, the impacts of the drought, opportunities for drought early warning, and an inventory of lessons learned. Key lessons learned include the following: 1) building partnerships during nondrought periods helps ensure that proper relationships are in place for a coordinated and effective drought response; 2) drought information providers must improve their understanding of the annual decision cycles of all relevant sectors, including, and beyond, direct impacts in agricultural sectors; and 3) ongoing monitoring of environmental conditions is vital to drought early warning, given that seasonal forecasts lack skill over the northern Great Plains.

Differences between two types of prediction skill estimates over Southern Africa are illustrated to better inform the users of seasonal precipitation forecasts over the region who desire assessments of forecast accuracy. Both seasonal precipitation prediction skill estimates for the African continent south of 15°S during the December–March rainy season are derived from the perfect-model method. The perfect-model method is based on a 40-member ensemble of Community Atmosphere Model version 5 simulations forced by observed time-evolving boundary conditions during 1920–2016. The first skill estimate is based on the verification of an ensemble mean forecast spanning many seasons and therefore unconditional on a single boundary forcing. The second skill estimate is based on the verification of an ensemble mean forecast for a single season and is therefore conditional on that year’s boundary forcing. Unconditional prediction skill calculated in 30-year increments for each of the 40 possible forecasts reveals: (1) large spread in skill among the individual forecasts for any given year and (2) temporal variations in skill for each forecast. The magnitude of conditional prediction skill varies greatly from 1 year to the next, revealing that the boundary conditions offer little prediction skill during some years and comparably large skill during others. The simultaneous behaviors of the El Niño–Southern Oscillation and the subtropical Indian Ocean Dipole are related to the largest conditional precipitation prediction skill years. Unconditional skill estimates may therefore mislead users of forecasts who desire assessments of forecast accuracy. Unconditional skill may be temporally unstable, and unlike conditional skill, is not representative of the skill for a given season.

Upper Colorado River basin streamflow has declined by roughly 20% over the last century of the instrumental period, based on estimates of naturalized flow above Lees Ferry. Here we assess factors causing the decline and evaluate the premise that rising surface temperatures have been mostly responsible. We use an event attribution framework involving parallel sets of global model experiments with and without climate change drivers. We demonstrate that climate change forcing has acted to reduce Upper Colorado River basin streamflow during this period by about 10% (with uncertainty range of 6%-14% reductions). The magnitude of the observed flow decline is found to be inconsistent with natural variability alone, and approximately one-half of the observed flow decline is judged to have resulted from long-term climate change. Each of three different global models used herein indicates that climate change forcing during the last century has acted to increase surface temperature (similar to+1.2 degrees C) and decrease precipitation (similar to-3%). Using large ensemble methods, we diagnose the separate effects of temperature and precipitation changes on Upper Colorado River streamflow. Precipitation change is found to be the most consequential factor owing to its amplified impact on flow resulting from precipitation elasticity (percent change in streamflow per percent change in precipitation) of similar to 2. We confirm that warming has also driven streamflow declines, as inferred from empirical studies, although operating as a secondary factor. Our finding of a modest -2.5% degrees C-1 temperature sensitivity, on the basis of our best model-derived estimate, indicates that only about one-third of the attributable climate change signal in Colorado River decline resulted from warming, whereas about two-thirds resulted from precipitation decline.

A probabilistic approach to describing El Nino-Southern Oscillation (ENSO), based on consideration of the signal-to-noise ratio for the coupled ocean-atmosphere ENSO state, is presented. The ENSO signal is estimated using an ensemble of historical atmospheric model simulations forced by observed sea surface temperatures and sea ice during 1980-2016. The noise is estimated from departures of individual model realizations from their ensemble average when subjected to identical forcing. It is found that this atmospheric noise effect is substantial and yields considerable uncertainty in detecting the true coupled ENSO mode. This uncertainty exceeds analysis errors by an order of magnitude. Greater atmospheric noise is found to prevail during El Nino than La Nina, suggesting that the intensity of the monitored ENSO state during El Nino is prone to greater misattribution. Our results demonstrate that a deterministic state estimate of ENSO conditions may not be representative of the true real-time coupled ENSO mode.