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Mesoscale convective systems (MCSs) are the dominant rainfall producer over the United States during the warm season, causing natural disasters and severe weather every year. Global climate models have large uncertainty in projecting precipitation changes in the future climate. Here, a simple Lagrangian parcel model is used to investigate the impact of global warming on MCS initiation and growth. The single-column parcel model projects a mean precipitation decrease over the central United States and an increase to its east, in agreement with the CMIP5 model projection. It also highlights the crucial role of current climate mean-state model bias in exaggerating the change in future mean precipitation projection by 25%. As for convective population, the model captures the decreased occurrence frequency of weak to moderate convection and increased frequency of strong convection due to the increased CAPE and CIN, in agreement with convection-permitting regional simulations. Novel parameterizations of gust-front propagation speed and subsidence strength are developed as guided by cloud-resolving simulations. The multicolumn parcel model employing those parameterizations captures readily the cold pool–induced upscale growth feature. It simulates smaller mesoscale clusters over the central United States under global warming due to gust-front slowdown and subsidence strength enhancement, which are further attributed to land aridity–induced weakening of initial accumulated precipitation and strengthening of updraft speed, respectively. That said, mesoscale clusters could become bigger under more favorable conditions in future climate, including boundary layer moistening, convection lifetime lengthening, and cold pool mechanical-lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes. [ABSTRACT FROM AUTHOR]

The northeastern United States (NEUS) is a densely populated region with a number of major cities along the climatological storm track. Despite its economic and social importance, as well as the area's vulnerability to flooding, there is significant uncertainty around future trends in extreme precipitation over the region. Here, we undertake a regional study of the projected changes in extreme precipitation over the NEUS through the end of the twenty-first century using an ensemble of high-resolution, dynamically downscaled simulations from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) project. We find that extreme precipitation increases throughout the region, with the largest changes in coastal regions and smaller changes inland. These increases are seen throughout the year, although the smallest changes in extreme precipitation are seen in the summer, in contrast to earlier studies. The frequency of heavy precipitation also increases such that there are relatively fewer days with moderate precipitation and relatively more days with either no or strong precipitation. Averaged over the region, extreme precipitation increases by +3%–5% °C−1 of local warming, with the largest fractional increases in southern and inland regions and occurring during the winter and spring seasons. This is lower than the +7% °C−1 rate expected from thermodynamic considerations alone and suggests that dynamical changes damp the increases in extreme precipitation. These changes are qualitatively robust across ensemble members, although there is notable intermodel spread associated with models' climate sensitivity and with changes in mean precipitation. Together, the NA-CORDEX simulations suggest that this densely populated region may require significant adaptation strategies to cope with the increase in extreme precipitation expected at the end of the next century. Significance Statement: Observations show that the northeastern United States has already experienced increases in extreme precipitation, and prior modeling studies suggest that this trend is expected to continue through the end of the century. Using high-resolution climate model simulations, we find that coastal regions will experience large increases in extreme precipitation (+6.0–7.5 mm day−1), although there is significant intermodel spread in the trends' spatial distribution and in their seasonality. Regionally averaged, extreme precipitation will increase at a rate of ∼2% decade−1. Our results also suggest that the frequency of extreme precipitation will increase, with the strongest storms doubling in frequency per degree of warming. These results, taken with earlier studies, provide guidance to aid in resiliency preparation and planning by regional stakeholders. [ABSTRACT FROM AUTHOR]

The standardized precipitation index (SPI) measures meteorological drought relative to historical climatology by normalizing accumulated precipitation. Longer record lengths improve parameter estimates, but these longer records may include signals of anthropogenic climate change and multidecadal natural climate fluctuations. Historically, climate nonstationarity has either been ignored or incorporated into the SPI using a quasi-stationary reference period, such as the WMO 30-yr period. This study introduces and evaluates a novel nonstationary SPI model based on Bayesian splines, designed to both improve parameter estimates for stationary climates and to explicitly incorporate nonstationarity. Using synthetically generated precipitation, this study directly compares the proposed Bayesian SPI model with existing SPI approaches based on maximum likelihood estimation for stationary and nonstationary climates. The proposed model not only reproduced the performance of existing SPI models but improved upon them in several key areas: reducing parameter uncertainty and noise, simultaneously modeling the likelihood of zero and positive precipitation, and capturing nonlinear trends and seasonal shifts across all parameters. Further, the fully Bayesian approach ensures all parameters have uncertainty estimates, including zero precipitation likelihood. The study notes that the zero precipitation parameter is too sensitive and could be improved in future iterations. The study concludes with an application of the proposed Bayesian nonstationary SPI model for nine gauges across a range of hydroclimate zones in the United States. Results of this experiment show that the model is stable and reproduces nonstationary patterns identified in prior studies, while also indicating new findings, particularly for the shape and zero precipitation parameters. SIGNIFICANCE STATEMENT: We typically measure how bad a drought is by comparing it with the historical record. With long-term changes in climate or other factors, however, a typical drought today may not have been typical in the recent past. The purpose of this study is to build a model that measures drought relative to a changing climate. Our results confirm that the model is accurate and captures previously noted climate change patterns}a drier western United States, a wetter eastern United States, earlier summer weather, and more extreme wet seasons. This is significant because this model can improve drought measurement and identify recent changes in drought. [ABSTRACT FROM AUTHOR]

The Indian Ocean tripole (IOT) is an independent mode of ocean–atmosphere circulation centered on the tropical Indian Ocean. This study explores the physical mechanisms of the IOT affecting the western United States climate variation during the boreal summer. We find that the IOT is significantly correlated with both western United States summer surface temperature and precipitation anomalies. During positive IOT events, the westerly wind anomalies over the northern Indian Ocean are intensified by two cross-equator airflows over the tropical eastern Indian Ocean and the east coast of Africa. The resulting convergence of air over the northern Bay of Bengal–Indochina Peninsula–northern South China Sea (NBB–IP–NSCS) region (15°–25°N, 80°–125°E) exacerbates the surplus precipitation there. Serving as a heat source, these NBB–IP–NSCS precipitation anomalies can excite a circumglobal teleconnection (CGT)-like pattern that propagates eastward from west-central Asia toward North America along the Asia subtropical westerly jet, further influencing local circulation anomalies. Development of strong anticyclonic circulation over the western United States enhances descending motion and divergence there, resulting in negative precipitation anomalies. This circulation anomaly also induces the diabatic heating anomalies through allowing more solar radiation to reach the ground surface, further increasing the surface temperature anomalies. Meanwhile, the increased tropospheric temperature also raises local surface temperatures by modulating the adiabatic air expansion and compression. Ultimately, the CGT-like pattern associated with NBB–IP–NSCS precipitation anomalies sets up an atmospheric bridge by which the IOT can impact summer climate in the western United States. [ABSTRACT FROM AUTHOR]

This study investigates how extreme precipitation scales with dewpoint temperature across the northeastern United States, both in the observational record (1948–2020) and in a set of downscaled climate projections in the state of Massachusetts (2006–99). Spatiotemporal relationships between dewpoint temperature and extreme precipitation are assessed, and extreme precipitation–temperature scaling rates are evaluated on annual and seasonal scales using nonstationary extreme value analysis for annual maxima and partial duration series, respectively. A hierarchical Bayesian model is then developed to partially pool data across sites and estimate regional scaling rates, with uncertainty. Based on the observations, the estimated annual scaling rate is 5.5% °C−1, but this varies by season, with most nonzero scaling rates in summer and fall and the largest rates (∼7.3% °C−1) in the summer. Dewpoint temperatures and extreme precipitation also exhibit the most consistent regional relationships in the summer and fall. Downscaled climate projections exhibited different scaling rates compared to the observations, ranging between −2.5% and 6.2% °C−1 at an annual scale. These scaling rates are related to the consistency between trends in projected precipitation and dewpoint temperature over the twenty-first century. At the seasonal scale, climate models project larger scaling rates for the winter compared to the observations (1.6% °C−1). Overall, the observations suggest that extreme daily precipitation in the Northeast only thermodynamic scales with dewpoint temperature in the warm season, but climate projections indicate some degree of scaling is possible in the cold season under warming. Significance Statement: A warmer climate will likely result in the intensification of extreme precipitation, with the potential to enhance flood and stormwater risk. However, the relationship between extreme precipitation and temperature (i.e., the precipitation–temperature scaling rate) remains uncertain, particularly at regional scales, inhibiting societal adaptation to extreme events. Using observations and climate projections of daily precipitation and dewpoint temperature across the northeastern United States, we demonstrate that extreme daily precipitation does indeed scale with dewpoint temperature, but the rate of scaling varies by season, with the strongest relationship in the warm season. [ABSTRACT FROM AUTHOR]

Both the intensities of individual extreme rainfall events and the frequency of such events are important for infrastructure planning. We develop a new statistical extreme value model, the PGEV model, which makes it possible to use high-quality annual maximum series data instead of less well-checked daily data to estimate trends in intensity and frequency separately. The method is applied to annual maximum data from Vol. 10 of NOAA Atlas 14, dating from approximately 1900 to 2014, showing that in the majority of 333 rain gauge stations in the northeastern United States the frequency of extreme rainfall events increases as mean temperature increases, but that there is little evidence of trends in the distribution of the intensities of individual extreme rainfall events. The median of the frequency trends corresponds to extreme rainfall becoming 83% more frequent for each 1°C of temperature increase. Naturally, increasing trends in frequency also increase the yearly or decadal risks of very extreme rainfall events. Three other large areas in the contiguous United States, the Midwest, the Southeast, and Texas, are also studied, and show similar but weaker trends than those in the Northeast. Significance Statement: Climate change can increase extreme daily rainfall by making individual extreme rainfall events more frequent, more intense, or both. We develop new methods based on extreme value statistics to predict to what extent these three scenarios will occur. Our aim is to inform infrastructure planning, both for protection against high-impact catastrophes and for local planning of roads and sewers. The new methods allow using high-quality annual maximum series for prediction of frequency, instead of partial duration series. In the northeastern United States, extreme daily rainfall events are becoming more frequent with little evidence of increasing trends in the distribution of intensities of individual extreme daily rainfall events. For many stations the frequency increase exceeds 150% for each 1°C of temperature increase. [ABSTRACT FROM AUTHOR]

Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological datasets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded datasets over the conterminous United States. Compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5–2.5 day increase in mean wet spell length, 30% low bias in 20-yr return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation dataset is developed that omits the time adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new dataset is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. These changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate change applications across the CONUS. [ABSTRACT FROM AUTHOR]