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Climate models project increasing precipitation intensity but decreasing frequency as greenhouse gases increase. However, the exact mechanism for the frequency decrease remains unclear. Here we investigate this by analyzing hourly data from regional climate change simulations with 4 km grid spacing covering most of North America using the Weather Research and Forecasting model. The model was forced with present and future boundary conditions, with the latter being derived by adding the CMIP5 19-model ensemble mean changes to the ERA-interim reanalysis. The model reproduces well the observed seasonal and spatial variations in precipitation frequency and histograms, and the dry interval between rain events over the contiguous US. Results show that overall precipitation frequency indeed decreases during the warm season mainly due to fewer light-moderate precipitation (0.1 < P ≤ 2.0 mm/h) events, while heavy (2 < P ≤ 10 mm/h) to very heavy precipitation (P > 10 mm/h) events increase. Dry spells become longer and more frequent, together with a reduction in time-mean relative humidity (RH) in the lower troposphere during the warm season. The increased dry hours and decreased RH lead to a reduction in overall precipitation frequency and also for light-moderate precipitation events, while water vapor-induced increases in precipitation intensity and the positive latent heating feedback in intense storms may be responsible for the large increase in intense precipitation. The size of intense storms increases while their number decreases in the future climate, which helps explain the increase in local frequency of heavy precipitation. The results generally support a new hypothesis for future warm-season precipitation: each rainstorm removes ≥7% more moisture from the air per 1 K local warming, and surface evaporation and moisture advection take slightly longer than currently to replenish the depleted moisture before the next storm forms, leading to longer dry spells and a reduction in precipitation frequency, as well as decreases in time-mean RH and vertical motion.

Climate change is causing increases in extreme rainfall across the United States. This study uses observations and high-resolution modelling to show that rainfall changes related to rising temperatures depend on the available atmospheric moisture. Extreme precipitation intensities have increased in all regions of the Contiguous United States (CONUS) 1 and are expected to further increase with warming at scaling rates of about 7% per degree Celsius (ref.  2 ), suggesting a significant increase of flash flood hazards due to climate change. However, the scaling rates between extreme precipitation and temperature are strongly dependent on the region, temperature 3 , and moisture availability 4 , which inhibits simple extrapolation of the scaling rate from past climate data into the future 5 . Here we study observed and simulated changes in local precipitation extremes over the CONUS by analysing a very high resolution (4 km horizontal grid spacing) current and high-end climate scenario that realistically simulates hourly precipitation extremes. We show that extreme precipitation is increasing with temperature in moist, energy-limited, environments and decreases abruptly in dry, moisture-limited, environments. This novel framework explains the large variability in the observed and modelled scaling rates and helps with understanding the significant frequency and intensity increases in future hourly extreme precipitation events and their interaction with larger scales.

Destructive and costly flooding can occur when warm storm systems deposit substantial rain on extensive snowcover1–6, as observed in February 2017 with the Oroville Dam crisis in California7. However, decision-makers lack guidance on how such rain-on-snow (ROS) flood risk may respond to climate change. Here, daily ROS events with flood-generating potential8 are simulated over western North America for a historical (2000–2013) and future (forced under Representative Concentration Pathway 8.59) period with the Weather Research and Forecasting model; 4 km resolution allows the basin-scale ROS flood risk to be assessed. In the warmer climate, we show that ROS becomes less frequent at lower elevations due to snowpack declines, particularly in warmer areas (for example, the Pacific maritime region). By contrast, at higher elevations where seasonal snowcover persists, ROS becomes more frequent due to a shift from snowfall to rain. Accordingly, the water available for runoff10 increases for 55% of western North American river basins, with corresponding increases in flood risk of 20–200%, the greatest changes of which are projected for the Sierra Nevada, the Colorado River headwaters and the Canadian Rocky Mountains. Thus, flood control and water resource planning must consider ROS to fully quantify changes in flood risk with anthropogenic warming.

Mesoscale convective system (MCS)-organized convective storms with a size of ~100 km have increased in frequency and intensity in the USA over the past 35 years 1 , causing fatalities and economic losses 2 . However, their poor representation in traditional climate models hampers the understanding of their change in the future 3 . Here, a North American-scale convection-permitting model which is able to realistically simulate MSCs 4 is used to investigate their change by the end-of-century under RCP8.5 (ref. 5 ). A storm-tracking algorithm 6 indicates that intense summertime MCS frequency will more than triple in North America. Furthermore, the combined effect of a 15–40% increase in maximum precipitation rates and a significant spreading of regions impacted by heavy precipitation results in up to 80% increases in the total MCS precipitation volume, focussed in a 40 km radius around the storm centre. These typically neglected increases substantially raise future flood risk. Current investments in long-lived infrastructures, such as flood protection and water management systems, need to take these changes into account to improve climate-adaptation practices. Limitations with climate models have previously prevented accurate diagnosis of future changes in mesoscale convective systems (MCSs). A convection-permitting model now indicates that summer MCSs will triple by 2100 in the United States, with a corresponding increase in rainfall rates and areal extent.

As one of the major producers of extreme precipitation, mesoscale convective systems (MCSs) have received much attention. Recently, MCSs over several hotpots, including the Sahel and US Great Plains, have been found to intensify under global warming. However, relevant studies on the East Asian rainband, another MCS hotpot, are scarce. Here, by using a novel rain‐cell tracking algorithm on a high spatiotemporal resolution satellite precipitation product, we show that both the frequency and intensity of MCSs over the East Asian rainband have increased by 21.8% and 9.8% respectively over the past two decades (2000–2021). The more frequent and intense MCSs contribute nearly three quarters to the total precipitation increase. The changes in MCSs are caused by more frequent favorable large‐scale water vapor‐rich environments that are likely to increase under global warming. The increased frequency and intensity of MCSs have profound impacts on the hydroclimate of East Asia, including producing extreme events such as severe flooding. Plain Language Summary Mesoscale convective systems (MCSs), accounting for more than half of the total rainfall in the East Asian rainband, frequently generate high‐impact extreme weather events, such as flooding. In the summer of 2020, large regions of East Asia suffered extensive flooding and damage. Therefore, understanding the long‐term changes of MCSs is crucial to gain insights into how extreme weather may change in the context of global warming. However, compared to several other MCS hotpots, the investigation of long‐term changes of MCSs is scarce over East Asia. Here, based on a high spatiotemporal resolution satellite precipitation product and a novel MCS tracking method, we find that MCSs have become more frequent and intense in the East Asian rainband and accounted for three quarters of the total rainfall increase during 2000–2021. It is further found that increases in atmospheric total column water vapor, which is mainly due to increased temperature caused by anthropogenic forcing, leads to more frequent large‐scale water vapor‐rich environments that are responsible for the intensification of MCSs. As water vapor increases with global warming, it is very likely that MCSs will continue to intensify in this region into the future. Key Points Mesoscale convective systems (MCSs) have become more frequent and intense in the East Asian rainband over the past two decades The significant increase of MCS precipitation accounted for three quarters of the total rainfall increase during 2000–2021 The increase of atmospheric total column water vapor, mainly driven by anthropogenic forcing, leads to more favorable environments for MCSs

Climate change increases the frequency and intensity of extreme precipitation, which in combination with rising population enhances exposure to major floods. An improved understanding of the atmospheric processes that cause extreme precipitation events would help to advance predictions and projections of such events. To date, such analyses have typically been performed rather unsystematically and over limited areas (e.g., the U.S.) which has resulted in contradictory findings. Here we present the Multi-Object Analysis of Atmospheric Phenomenon algorithm that uses a set of 12 common atmospheric variables to identify and track tropical and extra-tropical cyclones, cut-off lows, frontal zones, anticyclones, atmospheric rivers (ARs), jets, mesoscale convective systems (MCSs), and equatorial waves. We apply the algorithm to global historical data between 2001–2020 and associate phenomena with hourly and daily satellite-derived extreme precipitation estimates in major climate regions. We find that MCSs produce the vast majority of extreme precipitation in the tropics and some mid-latitude land regions, while extreme precipitation in mid and high-latitude ocean and coastal regions are dominated by cyclones and ARs. Importantly, most extreme precipitation events are associated with phenomena interacting across scales that intensify precipitation. These interactions are a function of the intensity (i.e., rarity) of extreme events. The presented methodology and results could have wide-ranging applications including training of machine learning methods, Lagrangian-based evaluation of climate models, and process-based understanding of extreme precipitation in a changing climate.

Straight line winds (SLWs), or non-tornadic thunderstorm winds, are causing widespread damage in many regions around the world. These powerful gusts are associated with strong downdraughts in thunderstorms, rear inflow jets and mesovortices. Despite their significance, our understanding of climate change effects on SLWs remains limited. Here, focusing on the central USA, a global hot spot for SLWs, I use observations, high-resolution modelling and theoretical considerations to show that SLWs have intensified over the past 40 years. Theoretical considerations suggest that SLWs should intensify at a rate of ~7.5% °C−1, yet the observed rates show a more pronounced increase of ~13% °C−1. The simulation results indicate a 4.8 ± 1.2-fold increase in the geographical extent affected by SLWs during the study period. These findings underscore the importance of incorporating intensifying SLWs into climate change adaptation planning to ensure the development of resilient future infrastructure.Non-tornadic thunderstorm winds are associated with particularly strong damages. Here, the author assesses changes in these winds in the central USA and shows that they have intensified stronger than other extreme winds over the past decades, while the affected area increased 4.8-fold.

An accurate understanding of the current and future water cycle over the Third Pole is of great societal importance, given the role this region plays as a water tower for densely populated areas downstream. An emerging and promising approach for skillful climate assessments over regions of complex terrain is kilometer-scale climate modeling. As a foundational step towards such simulations over the Third Pole, we present a multi-model and multi-physics ensemble of kilometer-scale regional simulations for the hydrological year of October 2019 to September 2020. The ensemble consists of 13 simulations performed by an international consortium of 10 research groups, configured with a horizontal grid spacing ranging from 2.2 to 4 km covering all of the Third Pole region. These simulations are driven by ERA5 and are part of a Coordinated Regional Climate Downscaling EXperiment Flagship Pilot Study on Convection-Permitting Third Pole. The simulations are compared against available gridded and in-situ observations and remote-sensing data, to assess the performance and spread of the model ensemble compared to the driving reanalysis during the cold and warm seasons. Although ensemble evaluation is hindered by large differences between the gridded precipitation datasets used as a reference over this region, we show that the ensemble improves on many warm-season precipitation metrics compared with ERA5, including most wet-day and hour statistics, and also adds value in the representation of wet spells in both seasons. As such, the ensemble will provide an invaluable resource for future improvements in the process understanding of the hydroclimate of this remote but important region.

Examining large‐scale projected changes in streamflow and flood extent (e.g., inundation) for Alaska is essential for raising awareness of flood hazards under a changing climate and supporting broad‐scale adaptation planning. Therefore, we examine projected changes in peak streamflow timing and magnitude using a physically based hydrologic model. For model inputs, we utilize climate simulations conducted at 4‐km horizontal grid spacing over Alaska from 2005 to 2016, providing a historical and future pseudo‐global warming scenario. Analysis of hydrographs reveals the peak timing shifts slightly earlier in the year for most of Alaska's streams. The change in peak magnitude is more heterogeneous across the state, with the northernmost region showing the highest projected increases. The changes in timing are driven by temperature, while precipitation and temperature drive the changes in magnitude. These changes are then transformed into inundation maps, showing a similar albeit more muted pattern compared to the changes in magnitude. Plain Language Summary This study examines how the timing and magnitude of median daily streamflows are projected to change across all streams in Alaska under climate change. Much of Alaska shows a shift in the peak timing to earlier in the year, with the highest increase in peak magnitude observed for the northernmost regions. The changes in timing are driven by temperature, while precipitation and temperature drive the changes in magnitude. These changes are then transformed into inundation maps, showing a similar albeit more muted pattern compared to the changes in magnitude. Overall, we provide an assessment of the changes in flood extent across Alaska under warming climate conditions. Key Points We perform hydrologic modeling across Alaska and assess the change in flood magnitude and inundation using km‐scale modeling of the climate Temperature and precipitation (temperature) shift hydrograph peak magnitude (timing) under pseudo‐global warming simulations for Alaska Higher precipitation and higher evapotranspiration compensate, resulting in only a small increase in flooding across Alaska

Global warming alters the hydrological cycle, increasing heavy rainfall events worldwide. In October 2024, Valencia (Spain) experienced rainfall accumulations in a few hours surpassing annual averages (771.8 mm in 16 h in the official weather station at Turís) and breaking the record for one hour rainfall accumulation in Spain (184.6 mm), resulting in 230 fatalities. Here, we present a physical-based attribution study employing a km-scale pseudo-global warming storyline approach to assess the contribution of anthropogenic climate change. We show that present-day conditions led to a 20% °C⁻¹ increase in 1-hour rainfall intensity, exceeding Clausius-Clapeyron scaling. This intensification was driven by enhanced atmospheric moisture from warmer sea surface temperatures, leading to increased convective available potential energy, stronger updrafts, and microphysical changes including elevated graupel concentrations. These results demonstrate that anthropogenic climate change could intensify the occurrence of flash-floods in the Western Mediterranean region: in this particular case, it intensified the 6-h rainfall rate by 21%, amplified the area with total rainfall above 180 mm by 55%, and increased the volume of total rain within the Jucar River catchment by 19% compared to the pre-industrial era. This study highlights the urgent need for effective adaptation strategies and improved urban planning to reduce the growing risks of hydrometeorological extremes in a rapidly warming world. This study reveals how climate change altered storm dynamics during the 2024 Valencia floods. Warmer seas fueled stronger updrafts, increasing rainfall intensity by 20% and significantly amplifying flood severity.