Extreme Days Research Articles

Unravelling the complex interplay between daily and sub-daily rainfall extremes in different climates

Understanding short-duration intense rainfall is crucial for mitigating flash floods, landslides, soil erosion, and pollution incidents. Yet, most observations from rain gauges are only available at the daily resolution. We use the new Global Sub Daily Rainfall dataset to explore extreme rainfall at both daily and sub-daily durations worldwide. Employing Single Gauge Analysis (SGA) and pioneering global-scale Regional Frequency Analysis (RFA), we reveal for the first time how Generalized Extreme Value Distribution (GEV) parameters change across climates and data durations (1h, 3h, 6h, 24h, and daily). This marks the first-ever near-global-scale RFA, made possible by the development of an algorithm that automates RFA on observed rainfall datasets. We compare our results with GEV applied to a gridded rainfall reanalysis (ERA5). Our key findings are that: 1) using ERA5, return levels are significantly underestimated across all climates for 1h rainfall and across all data durations for gauges in the tropical climate region. Even when accounting for differences between point and areal estimates, the median 1h return level estimates are approximately 40% lower compared to RFA. We therefore strongly advise against the use of reanalysis gridded rainfall for studying these extremes. 2) While most gauges show similar return levels with RFA or SGA, some differ significantly, and either method may yield the highest values. Thus, we strongly recommend using both SGA and RFA simultaneously to estimate return levels for a robust risk assessment in flood infrastructure design. 3) The interaction between daily and sub-daily GEV shape parameters varies across climate regions, rendering a universal method for inferring sub-daily rainfall extremes from daily extremes (e.g., using Intensity-Duration-Frequency curves) impractical. Our research provides innovative methodological insights that warrant consideration in future studies on rainfall extremes. Our results not only benefit local stakeholders globally but also serve as a crucial validation tool for the rising number of convection-permitting climate model experiments conducted worldwide.

Diurnal Cycle of Precipitation and Extremes in Nepal

This study examines the spatio-temporal distribution of hourly precipitation across Nepal using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data from 2015 to 2021. The findings indicate that hourly precipitation intensity during the monsoon season can reach up to 0.7 mm/hr, while pre-monsoon intensities peak at 0.2 mm/hr. These intensities are predominantly concentrated in mid- and low-elevation areas of central and eastern Nepal. Post-monsoon and winter seasons exhibit high-intensity precipitation patches over high-elevation regions in the western, central, and eastern peripheries of the country. The annual distribution of hourly precipitation shows a pronounced peak during the monsoon season, indicating that a significant portion of the total annual precipitation occurs during this period. Extreme precipitation events follow a similar seasonal distribution, with monsoon extremes exceeding 15 mm/hr. The diurnal cycle of monsoonal precipitation shows unique characteristics, peaking at 0.65 mm/hr around midnight and decreasing to 0.2 mm/hr by late morning, then increasing steadily in the afternoon and evening. Additionally, the study contrasts hourly heavy precipitation extremes (≥10 mm/hr or day) with daily extremes, noting that hourly extremes, despite being accumulated into daily measures, present a higher frequency, and pose greater short-term hazard risks. This analysis over a seven-year period suggests the need for continued research to determine spatial and temporal trends in hourly versus daily extreme precipitation patterns, particularly in the context of climate change and its impacts on regional hydrology and meteorology.

Projected changes in heat, extreme precipitation, and their spatially compound events over China’s coastal lands and seas through a high-resolution climate models ensemble

China’s coastal lands and seas are highly susceptible to the changing environment due to their dense population and frequent economic activities. These areas experience more significant impacts from climate change-induced extreme events than elsewhere. The most noticeable effects of climate change are extreme high temperatures and extreme precipitation. We employ an ensemble of RCMs (Regional Climate Models) to investigate and project changes in temperature, precipitation, and Compound Heat-Precipitation Extreme events (CHPEs) over selected China’s coastal lands and seas for both historical (1985–2004) and future periods (2080–2099). The multi-model ensemble projects that daily temperature extremes will increase by 2.9 °C to 5.4 °C across China’s coastal lands and seas, with land areas showing a higher temperature increase than marine areas. Extreme precipitation shows a high geographical heterogeneity with a 2.8–3.9 mm d−1 reduction over the 15–25°N marine areas while a 2.2–5.4 mm d−1 increment over the 25°N-35°N land areas. We use the Clausius–Clapeyron relationship to reveal that the peak of daily extreme precipitation will increase by 2–7 mm d−1 and the temperature at which extreme precipitation peaks will increase by 2 °C to 6 °C by warming. The land area of 25–30°N has the highest peak precipitation increase of 9.87 mm d−1 and a peak temperature increase of 6 °C. As precipitation extremes intensify with daily temperature extremes increase, CHPEs are projected to occur more frequently over both land and marine areas. Compared with the historical period, the frequency of CHPEs will increase by 40.9%-161.2% over marine areas, and by 36.2%-163.6% over land areas in the future. The 15–20°N area has the highest frequency increase of CHPE events, and the 25–30°N area has the largest difference in frequency increase under two different scenarios. It indicated that the 25–30°N area will be more easily affected by climate change.

Non-linear relationships between daily temperature extremes and US agricultural yields uncovered by global gridded meteorological datasets

Global agricultural commodity markets are highly integrated among major producers. Prices are driven by aggregate supply rather than what happens in individual countries in isolation. Estimating the effects of weather-induced shocks on production, trade patterns and prices hence requires a globally representative weather data set. Recently, two data sets that provide daily or hourly records, GMFD and ERA5-Land, became available. Starting with the US, a data rich region, we formally test whether these global data sets are as good as more fine-scaled country-specific data in explaining yields and whether they estimate similar response functions. While GMFD and ERA5-Land have lower predictive skill for US corn and soybeans yields than the fine-scaled PRISM data, they still correctly uncover the underlying non-linear temperature relationship. All specifications using daily temperature extremes under any of the weather data sets outperform models that use a quadratic in average temperature. Correctly capturing the effect of daily extremes has a larger effect than the choice of weather data. In a second step, focusing on Sub Saharan Africa, a data sparse region, we confirm that GMFD and ERA5-Land have superior predictive power to CRU, a global weather data set previously employed for modeling climate effects in the region.

Biogeographic patterns of daily wildfire spread and extremes across North America

IntroductionClimate change is predicted to increase the frequency of extreme single-day fire spread events, with major ecological and social implications. In contrast with well-documented spatio-temporal patterns of wildfire ignitions and perimeters, daily progression remains poorly understood across continental spatial scales, particularly for extreme single-day events (“blow ups”). Here, we characterize daily wildfire spread across North America, including occurrence of extreme single-day events, duration and seasonality of fire and extremes, and ecoregional climatic niches of fire in terms of Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD) annual climate normals.MethodsRemotely sensed daily progression of 9,636 wildfires ≥400 ha was used to characterize ecoregional patterns of fire growth, extreme single-day events, duration, and seasonality. To explore occurrence, extent, and impacts of single-day extremes among ecoregions, we considered complementary ecoregional and continental extreme thresholds (Ecoregional or Continental Mean Daily Area Burned + 2SD). Ecoregional spread rates were regressed against AET and CWD to explore climatic influence on spread.ResultsWe found three-fold differences in mean Daily Area Burned among 10 North American ecoregions, ranging from 260 ha day−1 in the Marine West Coast Forests to 751 ha day−1 in Mediterranean California. Ecoregional extreme thresholds ranged from 3,829 ha day−1 to 16,626 ha day−1, relative to a continental threshold of 7,173 ha day−1. The ~3% of events classified as extreme cumulatively account for 16–55% of total area burned among ecoregions. We observed four-fold differences in mean fire duration, ranging from 2.7 days in the Great Plains to 10.5 days in Northwestern Forested Mountains. Regions with shorter fire durations also had greater daily area burned, suggesting a paradigm of fast-growing short-duration fires in some regions and slow-growing long-duration fires elsewhere. CWD had a weak positive relationship with spread rate and extreme thresholds, and there was no pattern for AET.DiscussionRegions with shorter fire durations had greater daily area burned, suggesting a paradigm of fast-growing short-duration fires in some regions and slow-growing long-duration fires elsewhere. Although climatic conditions can set the stage for ignition and influence vegetation and fuels, finer-scale mechanisms likely drive variation in daily spread. Daily fire progression offers valuable insights into the regional and seasonal distributions of extreme single-day spread events, and how these events shape net fire effects.

ANALYSE DE L’INFLUENCE DES PARAMETRES METEOROLOGIQUES SUR LA PLUVIOMETRIE A KINSHASA

The understanding of meteorological parameters influencing rainfall is essential to effectively address environmental challenges and anticipate the consequences of extreme weather events. In this study, we analyze meteorological data provided by the Airways Authority of Kinshasa (RVA), covering the period from 1992 to 2019. This in-depth statistical analysis aims to better understand the factors that influence precipitation in the region and to identify relevant climate trends for anticipating future environmental changes. We used logistic regression to assess the impact of various climatic factors such as temperature (daily extremes), relative humidity, and atmospheric pressure on precipitation. The results indicate that minimum temperature, relative humidity, and atmospheric pressure show a strong correlation with rainfall in Kinshasa, highlighting their significant influence on the likelihood of precipitation. In contrast, maximum temperature has a limited impact, making it a less reliable indicator of rainfall in the city. The conclusions of this study provide valuable tools for local authorities in developing effective strategies for managing climate variations and predicting weather phenomena. These insights enable precise modeling of the impacts of meteorological fluctuations on ecosystems and urban infrastructure. Furthermore, the results of this research can guide local farmers in optimizing their agricultural practices by adjusting planting and harvesting schedules based on detailed weather forecasts, thereby minimizing economic losses and enhancing crop resilience to climate variations. Finally, this study provides climatologists with robust data for in-depth analysis of the effects of climate change on meteorological phenomena in Kinshasa. This knowledge is crucial for refining regional climate models and developing specific adaptation strategies for other urban areas in sub-Saharan Africa.

The Synoptic Relationship Between Days of Thermal Extremes and Pressure Systems During the Year 2020 in Iraq (Baghdad as An Example)

Temperature is one of the most important elements of weather and climate that directly and indirectly affects other weather and climatic phenomena. The study aims to daily detect thermal extreme days during the year 2020 over the months of the year for the maximum temperature (hot extreme days) at the Baghdad Climatic Station as well. The pressure systems varied in their strength or shallowness in each case. The study concluded that the number of days (cases) of thermal extremes during one month, with one day being unique in recording the highest or lowest extreme values, according to the prevailing lows or highs in the atmosphere, as the maximum extremes in the average maximum temperature (extreme hot days) during the study year were evident on the twenty-eighth day of July at a rate of (51.8) °C, followed by the high temperature extremes on the thirtieth day of the month. August, around (47.3)°C, and the cause of the extremes on the two days mentioned was the strongest pressure system controlling Iraq’s climate during the summer months, represented by the Indian seasonal low, and its deep appearance at the level of 850 millibars, accompanied by tropical air in the middle of the troposphere, that is, at the level of 500 millibars, in the form of a dent or barrier. My pressure, which increases the severity of the daily thermal extremes for that month, citing in our study the daily weather maps on each thermally extreme day.

Investigation of climate change impacts on daily streamflow extremes in Eastern Black Sea Basin, Turkey

This study was undertaken to evaluate how future uncertain climate-related hydrological responses and low and highflow frequencies affect local hydrology up to the 2100, depending on different Global Circulation Models (GCM) and different Concentration Scenarios (CS). To this end, daily total precipitation and daily mean temperature data were used for RCP4.5 and RCP8.5 CSs by employing GCMs (GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR). In the study, the years 1971–2000 were chosen as the baseline period. Future streamflow predictions were modeled by machine learning. The models were calibrated with baseline period data and future streamflow values were predicted by using the best Streamflow Prediction Model during the 2055s (2041–2070) and 2085s (2071–2100) periods. Flow Duration Curves from the streamflow predictions were obtained and by using them, discharges corresponding to 95% probability of exceedance for lowflow and 5% probability of exceedance for highflow were calculated. According to the findings, it is predicted that there will be decrease of approximately 21% in lowflow discharges and approximately 30% in highflow discharges. This prediction has suggested water resources managers to implement mitigation measures to climate change in Eastern Black Sea Basin and most probably in Türkiye, especially in the context of continuity of aquatic life and sustainable water supply. It is expected that the paper will take a very important place in estimating and evaluating the expected changes in basin hydrology in the coming years, especially in the highflows and lowflows, which are the key concepts in hydroelectric power generation and in flood management.

A novel approach combining satellite and in situ observations to estimate the daytime variation of land surface temperatures for all sky conditions

Land surface temperature (LST) and its diurnal variability are key to understanding the land-atmosphere interactions, hydrological processes and climate change. However, at any given point in time approximately half of the Earth's surface is covered by clouds. This restricts the availability of LST through satellite remote sensing, which works best under clear skies. However, in situ observations continue to monitor atmospheric conditions beneath the clouds that could complement satellite measurements during cloudy conditions. The present study explores a novel approach to estimate hourly LST during the daylight hours using remotely sensed surface solar absorption and in situ observations of daily LST extremes (maximum and minimum) together with an adaptive non-linear fitting approach. A learning algorithm trained against in-situ measurements of LST extrema and diurnal cycle of surface solar absorption together with the associated linear correlation between the two parameters, is used to estimate an optimized set of parameters to approximate hourly LST for each day during the daylight hours between sunrise and sunset. Results show that the method captures the intra-day variability of LST very well under most sky conditions with rms errors below 1.5 K.

On the response of daily precipitation extremes to local mean temperature in the Yangtze River basin

This study explores the response of daily extreme precipitation (PEX) to local mean temperatures (T) of the previous 0-day (T0), 1-day (T1), and 1 to 9-days (T1–9) in the Yangtze River basin (YRB) using the non-stationarity test, binning method and copula theory. Results show that the annual scaling rate of PEX with T during 1961–2020 exhibits positive, negative and positive changing trends from southwest to northeast with the maximum magnitudes in the west, and experiences significant abrupt change only at 14.1% grids. The PEX-T scaling relation in the entire study period is predominantly characterized by a peak pattern over the YRB, with precipitation peaking at about 8 °C in the western highlands and at 25 °C in the east-central lowlands. However, the use of T1–9 yields a positive PEX-T pattern at more grids compared to T1 and T0, which highlights the important role of atmospheric moisture residence time in minimizing cooling artefacts in the scaling estimation. Generally, the full scaling rate of PEX with T in the study period is larger in the west-central part but smaller in the eastern YRB, and more sensitive to antecedent temperatures, with the ranges of −8–20%/°C, −8–30%/°C and −10–30%/°C against T0, T1 and T1–9, respectively. As precipitation intensifies, the full scaling decreases in plateau-mountain areas but increases in low-lying regions, gradually approaching the Clausius-Clapeyron (CC) scaling and exhibiting a “super-CC, CC-like and sub-CC scaling” pattern from west to east. Additionally, heavier precipitation events are most likely to occur at higher local temperatures in most areas even with peak structure. These findings provide new insights into the PEX-T relationships, which is conducive to understanding future changes in precipitation extremes and assessing precipitation-related risks under climate change.

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

AbstractA number of studies have investigated the large‐scale drivers and upstream precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high‐impact weather and providing early warning of their possible development. However, operational applications of this developing knowledge base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large‐scale precursors of categorical weather events in an automated fashion and reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional‐ to national‐scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large‐scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how, for more continental‐scale applications, the regionally specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales, and regions.

Probability Models and Some Mathematical Techniques on Parameter Estimation for Daily Rainfall Extremes: Application to Daily Rainfall in Southern Thailand

Probability Models and Some Mathematical Techniques on Parameter Estimation for Daily Rainfall Extremes: Application to Daily Rainfall in Southern Thailand

A Bayesian non-asymptotic extreme value model for daily rainfall data

Motivated by the analysis of extreme rainfall data, we introduce a general Bayesian hierarchical model for estimating the probability distribution of extreme values of intermittent random sequences, a common problem in geophysical and environmental science settings. The approach presented here is derived under a set of assumptions different than those used in traditional extreme value (EV) theory. A hierarchical model structure is adopted for capturing the underlying variability in the yearly distribution of event magnitudes and occurrences, which are described through a latent temporal process. Focusing on daily rainfall extremes, the structure of the proposed model lends itself to incorporating prior geo-physical understanding of the rainfall process. By means of an extensive simulation study, we show that this methodology can significantly reduce quantile estimation uncertainty with respect to Bayesian formulations of traditional asymptotic EV methods, particularly in the case of relatively small samples. On the other hand, a negative bias is observed in estimated rainfall quantiles. We evaluate the tradeoff between bias and variance for extreme rainfall quantiles and show that the approach proposed here can be advantageous depending on the sample size available for training. The benefits of the approach are illustrated with an application to a large data set of 479 long daily rainfall historical records from across the conterminous United States. By comparing measures of in-sample and out-of-sample predictive accuracy, we find that the model structure developed here, combined with the use of all available observations for inference, can improve robustness with respect to overfitting to the specific sample in case of short datasets.

Analysis of the Temporal-Spatial Trend of Frequency of Daily Extremes Precipitation in Iran

Analysis of the Temporal-Spatial Trend of Frequency of Daily Extremes Precipitation in Iran

Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation

Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km.

Comparison of machine learning statistical downscaling and regional climate models for temperature, precipitation, wind speed, humidity and radiation over Europe under present conditions

AbstractThere are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a statistical downscaling model (SDM) based on machine learning and six regional climate models (RCMs) from EURO‐CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation under present climatic conditions. The study is conducted at a continental scale over Europe, with a spatial resolution of 0.11° and daily data. Both the SDM and the RCMs are driven by the ERA‐Interim reanalysis, and observations are taken from the gridded dataset E‐OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, a multivariable index (fire weather index) derived from the fundamental variables has been included. The SDM has better scores than the RCMs for all the evaluated metrics with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long‐term trends or the sensitivity of different methods to being driven by global climate models, is needed.

Precipitation Extremes and Their Links with Regional and Local Temperatures: A Case Study over the Ottawa River Basin, Canada

In the context of global warming, the Clausius–Clapeyron (CC) relationship has been widely used as an indicator of the evolution of the precipitation regime, including daily and sub-daily extremes. This study aims to verify the existence of links between precipitation extremes and 2 m air temperature for the Ottawa River Basin (ORB, Canada) over the period 1981–2010, applying an exponential relationship between the 99th percentile of precipitation and temperature characteristics. Three simulations of the Canadian Regional Climate Model version 5 (CRCM5), at three different resolutions (0.44°, 0.22°, and 0.11°), one simulation using the recent CRCM version 6 (CRCM6) at “convection-permitting” resolution (2.5 km), and two reanalysis products (ERA5 and ERA5-Land) were used to investigate the CC scaling hypothesis that precipitation increases at the same rate as the atmospheric moisture-holding capacity (i.e., 6.8%/°C). In general, daily precipitation follows a lower rate of change than the CC scaling with median values between 2 and 4%/°C for the ORB and with a level of statistical significance of 5%, while hourly precipitation increases faster with temperature, between 4 and 7%/°C. In the latter case, rates of change greater than the CC scaling were even up to 10.2%/°C for the simulation at 0.11°. A hook shape is observed in summer for CRCM5 simulations, near the 20–25 °C temperature threshold, where the 99th percentile of precipitation decreases with temperature, especially at higher resolution with the CRCM6 data. Beyond the threshold of 20 °C, it appears that the atmospheric moisture-holding capacity is not the only determining factor for generating precipitation extremes. Other factors need to be considered, such as the moisture availability at the time of the precipitation event, and the presence of dynamical mechanisms that increase, for example, upward vertical motion. As mentioned in previous studies, the applicability of the CC scaling should not be generalised in the study of precipitation extremes. The time and spatial scales and season are also dependent factors that must be taken into account. In fact, the evolution of precipitation extremes and temperature relationships should be identified and evaluated with very high spatial resolution simulations, knowing that local temperature and regional physiographic features play a major role in the occurrence and intensity of precipitation extremes. As precipitation extremes have important effects on the occurrence of floods with potential deleterious damages, further research needs to explore the sensitivity of projections to resolution with various air temperature and humidity thresholds, especially at the sub-daily scale, as these precipitation types seem to increase faster with temperature than with daily-scale values. This will help to develop decision-making and adaptation strategies based on improved physical knowledge or approaches and not on a single assumption based on CC scaling.

Insights from the bias-corrected simulations of CMIP6 in India's future climate

In this study, we have used thirteen statistically downscaled bias-corrected General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under the Shared Socioeconomic Pathways (SSP) scenarios. Based on these models, the projected changes in mean rainfall and daily extremes (number of rainy days and simple daily intensity) during the southwest (SW) and northeast (NE) monsoon seasons in the near (2021–2050) and far future (2071–2100) under the SSP2-4.5 & SSP5-8.5 scenarios relative to the baseline period (1985–2014) are studied. In addition to the rainfall, the maximum, minimum, and mean temperature changes and their daily extremes are also examined. Our results show that the Multi-Model Mean (MMM) slightly underestimated (overestimated) the SW (NE) monsoon rainfall compared to the observed rainfall in the baseline period. A considerable increase in future rainfall during the SW monsoon season is noticed in central India, the Himalayan region, and over the northwestern parts of India. In the NE monsoon season, south peninsular India experiences more rainfall under SSP5-8.5 than SSP2-4.5. In general, both SSPs scenarios shows increase in monsoon rainfall from the mid-century onwards. Interestingly, by the end of the 21st century the number of rainy days are projected to reduce, whereas the intensity of rainfall projected to be drastically increase over many areas of India. Analysis of temperature revealed that there is increase in the projected warming with maximum temperature is around 4.5°C during summertime, with minimum temperatures of about 5°C in the wintertime in northern parts of India by the end of the 21st century. Under the SSP5-8.5 scenario, increase in the highest maximum temperatures is seen in the Himalayan region and elevated minimum temperature pattern over the Indo-Gangetic Basin (IGB) is noted. This increased warming may affect the agriculture, water, health, and power sectors severely.

A 4‐week advance in the growing season in Napa Valley, California,USA

AbstractThe growing season start and duration, along with other temperature‐related measures of importance to premium wine grapes in Napa Valley, California have changed as climate over the western United States has warmed. The growing season start has varied from year to year with a standard deviation of about 3 weeks, but over the 1958–2016 record a linear fit to the time sequence shows it advanced by more than 4 weeks. Over the study period, advances in the growing season were strongly influenced by temperature increases beginning in the late 1960s with warm anomalies generally persisting through recent years. The date upon which the growing season accumulated 1400 growing degree‐days also shifted earlier by about 4 weeks. Other measures swung to a warmer status, including the mean temperature of the last 45 days of the growing season, which warmed by over 1.5°C. Warming days and especially warming nights contributed to the growing season advance as well as trends towards warmer expressions of other viticultural measures. Years with earlier and warmer growing seasons experienced a substantial reduction in the number of daily cool extremes, and an increase in daily warm extremes, including the number of days whose temperature reaches or exceeds 35°C.

Future Increases in North American Extreme Precipitation in CMIP6 Downscaled with LOCA

Abstract A new set of CMIP6 data downscaled using the localized constructed analogs (LOCA) statistical method has been produced, covering central Mexico through southern Canada at 6-km resolution. Output from 27 CMIP6 Earth system models is included, with up to 10 ensemble members per model and 3 SSPs (245, 370, and 585). Improvements from the previous CMIP5 downscaled data result in higher daily precipitation extremes, which have significant societal and economic implications. The improvements are accomplished by using a precipitation training dataset that better represents daily extremes and by implementing an ensemble bias correction that allows a more realistic representation of extreme high daily precipitation values in models with numerous ensemble members. Over southern Canada and the CONUS exclusive of Arizona (AZ) and New Mexico (NM), seasonal increases in daily precipitation extremes are largest in winter (∼25% in SSP370). Over Mexico, AZ, and NM, seasonal increases are largest in autumn (∼15%). Summer is the outlier season, with low model agreement except in New England and little changes in 5-yr return values, but substantial increases in the CONUS and Canada in the 500-yr return value. One-in-100-yr historical daily precipitation events become substantially more frequent in the future, as often as once in 30–40 years in the southeastern United States and Pacific Northwest by the end of the century under SSP 370. Impacts of the higher precipitation extremes in the LOCA version 2 downscaled CMIP6 product relative to the LOCA downscaled CMIP5 product, even for similar anthropogenic emissions, may need to be considered by end-users.