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The study assesses the performance of 24 model runs from five COordinated Regional climate Downscaling Experiment (CORDEX) regional climate models (RCMs) in simulating East Africa’s spatio-temporal precipitation characteristics using a set of eight descriptors: consecutive dry days (CDD), consecutive wet days (CWD), simple precipitation intensity index (SDII), mean daily annual (pr_ANN), seasonal (pr_MAM and pr_OND) precipitation, and representatives of heavy precipitation (90p) and very intense precipitation (99p) events. Relatively better performing RCM runs are then used to assess projected precipitation changes (for the period 2071–2099 relative to 1977–2005) over the study domain under the representative concentration pathway (RCP) 8.5 scenario. The performance of RCMs is found to be descriptor and scope specific. Overall, RCA4 (r1i1p1) forced by CNRM-CERFACS-CNRM-CM5 and MPI-M-MPI-ESM-LR, REMO2009 (r1i1p1) forced by MPI-M-MPI-ESM-LR, and RCA4 (r2i1p1) forced by MPI-M-MPI-ESM-LR emerge as the top four RCM runs. We show that an ensemble mean of the top four model runs outperforms an ensemble mean of 24 model simulations and ensemble means for all runs in an RCM. Our analysis of projections shows a reduction (increase) in mean daily precipitation for MAM(OND), an increase(decrease) in CDD(CWD) events, and a general increase in SDII and the width of the right tail of the precipitation distribution (99p–90p). An increase in SDII and 99p–90p implies a possibility of occurrence of heavy and extreme precipitation incidences by the end of the twenty-first century. Our findings provide important information to support the region’s climate change adaptation and mitigation efforts.

The Global Precipitation Measurement (GPM) mission offers new opportunities for modeling a range of physical/hydrological processes at higher resolutions, especially for remote river systems where the hydrometeorological monitoring network is sparse and weather radar is not readily available. In this study, the recently released Integrated Multisatellite Retrievals for GPM [version 03 (V03) IMERG Final Run] product with high spatiotemporal resolution of 0.1° and 30 min is evaluated against ground-based reference measurements (at the 6-hourly, daily, and monthly time scales) over different terrestrial ecozones of southern Canada within a 23-month period from 12 March 2014 to 31 January 2016. While IMERG and ground-based observations show similar regional variations of mean daily precipitation, IMERG tends to overestimate higher monthly precipitation amounts over the Pacific Maritime ecozone. Results from using continuous as well as categorical skill metrics reveal that IMERG shows more satisfactory agreement at the daily and the 6-hourly time scales for the months of June–September, unlike November–March. In terms of precipitation extremes (defined by the 75th percentile threshold for reference data), apart from a tendency toward over-detection of heavy precipitation events, IMERG captured well the distribution of heavy precipitation amounts and observed wet/dry spell length distributions over most ecozones. However, low skill was found over large portions of the Montane Cordillera ecozone and a few stations in the Prairie ecozone. This early study highlights a potential applicability of V03 IMERG Final Run as a reliable source of precipitation estimates in diverse water resources and hydrometeorological applications for different regions in southern Canada.

Understanding sub‐daily precipitation extremes (SPEs) can provide scientific insights for taking effective measures to mitigate climate risks. Leveraging gauge observations at hourly precipitation in 2,312 meteorological stations and extreme sub‐daily precipitation indices (ESPIs), we investigate the changes of SPEs in flooding season of 1971–2022 in China. On country scale, the occurrences and intensity of SPEs have significantly increased and even accelerated since the 21st century, suggesting increases in 2010s by 15%–38% compared with that in 1970s. The SPE risks for 20‐year and 50‐year return‐period increased by 2–4 and 8–20 times in 2001–2022 compared with that in 1971–2000, respectively. Over 80% stations are found to have positive trends in all ESPIs. On regional scale, seven sub‐regions experienced significant increases in ESPIs with larger magnitudes in the East China. The enlarged 500‐hPa geopotential height, 700‐hPa pseudoequivalent potential temperature, 700‐hPa specific humidity, saturated vapor pressure and urbanization ratio may be bonded to more SPEs. Plain Language Summary Short‐term precipitation extremes have serious impacts on human safety, agriculture, energy, and infrastructure. It is very urgent to understand their variations and underlying mechanisms for policy‐makers to take effective approaches to mitigate extreme precipitation‐related risks. Here, we investigate the spatio‐temporal changes of sub‐daily precipitation extremes in the flooding season of 1971–2022 in China. We examine six metrics of extreme sub‐daily precipitation based on hourly observation data. We find that the occurrences and intensity of SPEs have significantly increased during the recent 52 years with acceleration since the 21st century on both country and regional level. The once‐in‐20‐year and once‐in‐50‐year events in 2001–2022 increased by 2–4 and 8–20 times compared with that in 1971–2000, respectively. The larger upward magnitudes of ESPIs are mainly located in the East China. In the era of rapid global warming, thermodynamic effects of abnormal atmospheric circulation and rapid urbanization possibly facilitate the occurrences of more SPEs in China. Key Points Sub‐daily precipitation extremes have substantially increased over China during 1971–2022, even accelerating since the 21st century Most of China exhibits consistent upward trends in all extreme sub‐daily precipitation indices with lager magnitudes in the East China Thermodynamic effects of abnormal atmospheric circulation and urbanization are possibly boned to increased sub‐daily precipitation extremes

We characterize trends in maximum seasonal daily precipitation (seasonal Rx1day), minimum (Tn), and maximum (Tx) daily temperatures during days with precipitation over continental Chile for the period 1979–2017, using surface stations and the AgERA5 gridded product derived from the ERA5 reanalysis dataset. We also examine seasonal trends of Sea Surface Temperature (SST), Precipitable Water (PW), Convective Available Potential Energy (CAPE), Eddy Kinetic Energy (EKE), Atmospheric Rivers (ARs) frequency, and upper air observations to seek possible mechanisms that explain precipitation trends. Our results show an increase in seasonal Rx1day during fall in the south part of Northern Chile (15–30°S) and during fall and winter in Austral Chile (45–57°S), and mostly negative trends in Central Chile (30–36°S), where a few locations with positive trends along the coast during summer. Temperature trends presented cooling patterns north of 33°S in almost all the seasons (< -2 °C/dec), while warming trends prevail south of 38°S (> 1 °C/dec). The highest values in Tn trends are obtained on the western slopes of the Andes around 30°S. We also explore temperature scaling in surface stations, finding strong positive super Clausius Clapeyron with Tn, especially between fall and spring in the 33–40°S region. Sounding observations in five stations across Chile suggest warming trends at 23.5°, 33°S, and 53°S, with a stabilization effect by enhanced warming in the upper troposphere, while presenting cooling trends in Puerto Montt (41.5°S). Seasonal trends in PW reveal moistening along southern Peru and northern Chile during spring and summer. Positive trends in CAPE are observed over 35–40°S (austral summer and fall) and the north Altiplano (autumn). SST analyses reveal strong cooling around 30°S in winter, which may explain the negative trends in seasonal Rx1day in central Chile. A warming spot on the northern Peruvian coast during fall may be responsible for humidification in front of Northern Chile, particularly during summer and fall. Positive EKE trends are detected south of 40°S, being stronger and reaching almost all of the coast during spring. ARs frequency unveils negative trends up to -5 days/dec during summer and positive trends of 1 day/dec in 40°- 50°S coastal regions during spring. More generally, the results presented here shed light on the main large-scale processes driving recent trends in precipitation extremes across continental Chile.

Downscaling is an effective technique to bridge the gap between climate model outputs and data requirements of most crop and hydrologic models for assessing local and site-specific climate change impacts, especially on future food security. However, downscaling of temporal sequences, extremes in daily precipitation, and handling of nonstationary precipitation in future conditions are considered common challenges for most statistical downscaling methods. This study reviewed the above key challenges in statistical downscaling and proposed potential solutions. Ten weather stations located across the globe were used as proof of concept. The use of a stochastic Markov chain to generate daily precipitation occurrences is an effective approach to simulate the temporal sequence of precipitation. Also, the downscaling of precipitation extremes can be achieved by adjusting the skewness coefficient of a probability distribution, as they are highly correlated. Nonstationarity in precipitation downscaling can be handled by adjusting parameters of a probability distribution according to future precipitation change signals projected by climate models. The perspectives proposed in this study are of great significance in using climate model outputs for assessing local and site-specific climate change impacts, especially on future food security.

This study utilizes Bayesian model averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA versus 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multimodel ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the southern United States, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new “probabilistic” gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj).

This work provides a significant contribution on the open debate in the climate community to establish the added value of very high-resolution configurations, characterized by a horizontal resolution below 4 km with respect to current state-of-the-art climate simulations (10–15 km). Specifically, it aims at assessing quantitative gains and losses in the performance of climate models caused by an enhancement in temporal and spatial resolution by evaluating the capability of different climate simulations in reproducing daily and sub-daily present precipitation dynamics over a complex orographic context such as the Alpine region. In this perspective, the results of three experiments (EURO-CORDEX ensemble mean, CCLM 8 and CCLM 2.2) at different spatial (~ 12, 8 and 2.2 km) and temporal (daily, 6 h and 3 h) scales are compared to gridded and point-scale observational datasets. Precipitation data are analyzed by mean of the Expert Team on Climate Change Detection and Indices indicators, as well as with statistical models able to evaluate the precipitation distribution and the extreme values for different durations of precipitation events. To objectively assess gains and losses in adopting high-resolution RCMs, data are elaborated assuming the distribution added value as metric, particularly focusing on the role of orography. The work returns, at daily scale, a gain in climate model performances moving from lower to higher horizontal resolution. At the same time, investigating the effect of the orography the simulation with the finest grid proves to better capture local precipitation dynamics at higher altitudes in terms of both sub-daily precipitation and extreme events.

Changes in soil moisture are likely to contribute to future changes in latent heat flux and various characteristics of daily precipitation. Such contributions during the second half of the twenty-first century are assessed using the simulations from the GLACE-CMIP5 experiment, applying a linear regression analysis to determine the magnitude of these contributions. As characteristics of daily precipitation, mean daily precipitation, the frequency of wet days and the intensity of precipitation on wet days are considered. Also, the frequency and length of extended wet and dry spells are studied. Particular focus is on the regional (for nine selected regions) as well as seasonal variations in the magnitude of the contributions of the projected differences in soil moisture to the future changes in latent heat flux and in the characteristics of daily precipitation. The results reveal the overall tendency that the projected differences in soil moisture contribute to the future changes in response to the anthropogenic climate forcing for all the meteorological variables considered here. These contributions are stronger and more robust (i.e., there are smaller deviations between individual climate models) for the latent heat flux than for the characteristics of daily precipitation. It is also found that the contributions of the differences in soil moisture to the future changes are generally stronger and more robust for the frequency of wet days than for the intensity of daily precipitation. Consistent with the contributions of the projected differences in soil moisture to the future changes in the frequency of wet days, soil moisture generally contributes to the future changes in the characteristics of wet and dry spells. The magnitude of these contributions does not differ systematically between the frequency and the length of such extended spells, but the contributions are generally slightly stronger for dry spells than for wet spells. Distinguishing between the nine selected regions and between the different seasons, it is found that the strength of the contributions of the differences in soil moisture to the future changes in the various meteorological variables varies by region and, in particular, by season. Similarly, the robustness of these contributions varies between the regions and in the course of the year. The importance of soil moisture changes for the future changes in various aspects of daily precipitation and other aspects of the hydrological cycle illustrates the need for a comprehensive and realistic representation of land surface processes and of land surface conditions in climate models.

The metropolitan region of São Paulo (MRSP), located in southeastern Brazil, is densely populated and economically significant for the country. Cold fronts crossing MRSP are crucial for the precipitation regime and can induce extreme rainfall events. Therefore, this study presents a climatology of the contribution of cold fronts to extreme rainfall events in the MRSP and their associated synoptic patterns, using long term (1960 to 2022) local observations to identify cold fronts and daily rainfall extremes, and a time window of 5 days (from 2 days before and after). The mean circulation patterns associated with these events are analyzed using gridded precipitation and reanalysis for a short period (1980–2022). For 1960–2022, a total of 2535 cold fronts and 1077 extremes rainfall events were identified, with 58% of the daily extremes being associated with cold fronts. These events mainly occur during the austral spring, although their daily mean precipitation is higher in the austral summer. The contribution of cold fronts to extreme precipitation exhibits a statistically significant decrease, contrasting with the increasing trend in daily rainfall extremes. This suggests the influence of other meteorological systems in increasing rainfall extremes. The synoptic patterns reveal an intense anticyclone located southwest of the main trough in the lower troposphere, intense cyclonic vorticity along the front at low levels, stronger upper troposphere divergence, and strong thickness gradients in the 500–1000 hPa layer affecting MRSP. Most extremes occur on the day before and the day of the cold front due to more favorable dynamic conditions for convective activity.