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This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.

Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system. This study analyses, to our knowledge for the first time, the impacts of climate extremes on yield anomalies of maize, soybeans, rice and spring wheat at the global scale using sub-national yield data and applying a machine-learning algorithm. We find that growing season climate factors-including mean climate as well as climate extremes-explain 20%-49% of the variance of yield anomalies (the range describes the differences between crop types), with 18%-43% of the explained variance attributable to climate extremes, depending on crop type. Temperature-related extremes show a stronger association with yield anomalies than precipitation-related factors, while irrigation partly mitigates negative effects of high temperature extremes. We developed a composite indicator to identify hotspot regions that are critical for global production and particularly susceptible to the effects of climate extremes. These regions include North America for maize, spring wheat and soy production, Asia in the case of maize and rice production as well as Europe for spring wheat production. Our study highlights the importance of considering climate extremes for agricultural predictions and adaptation planning and provides an overview of critical regions that are most susceptible to variations in growing season climate and climate extremes.

Estimates of observed long-term changes in daily precipitation globally have been limited due to availability of high-quality observations. In this study, a new gridded dataset of daily precipitation, called Rainfall Estimates on a Gridded Network (REGEN) V1–2019, was used to perform an assessment of the climatic changes in precipitation at each global land location (except Antarctica). This study investigates changes in the number of wet days (≥1 mm) and the entire distribution of daily wet- and all-day records, in addition to trends in annual and seasonal totals from daily records, between 1950 and 2016. The main finding of this study is that precipitation has intensified across a majority of land areas globally throughout the wet-day distribution. This means that when it rains, light, moderate, or heavy wet-day precipitation has become more intense across most of the globe. Widespread increases in the frequency of wet days are observed across Asia and the United States, and widespread increases in the precipitation intensity are observed across Europe and Australia. Based on a comparison of spatial pattern of changes in frequency, intensity, and the distribution of daily totals, we propose that changes in light and moderate precipitation are characterized by changes in precipitation frequency, whereas changes in extreme precipitation are primarily characterized by intensity changes. Based on the uncertainty estimates from REGEN, this study highlights all results in the context of grids with high-quality observations.

Marine heatwaves (MHWs) can cause devastating impacts to marine life. Despite the serious consequences of MHWs, our understanding of their drivers is largely based on isolated case studies rather than any systematic unifying assessment. Here we provide the first global assessment under a consistent framework by combining a confidence assessment of the historical refereed literature from 1950 to February 2016, together with the analysis of MHWs determined from daily satellite sea surface temperatures from 1982–2016, to identify the important local processes, large-scale climate modes and teleconnections that are associated with MHWs regionally. Clear patterns emerge, including coherent relationships between enhanced or suppressed MHW occurrences with the dominant climate modes across most regions of the globe – an important exception being western boundary current regions where reports of MHW events are few and ocean-climate relationships are complex. These results provide a global baseline for future MHW process and prediction studies. Impacts from marine heatwaves can be devastating, but understanding their causes is largely based on case studies. Here the authors carry out a global assessment of literature and sea surface temperatures to identify important local processes, climate modes and teleconnections that drive marine heatwaves regionally.

The global ocean has warmed substantially over the past century, with far-reaching implications for marine ecosystems1. Concurrent with long-term persistent warming, discrete periods of extreme regional ocean warming (marine heatwaves, MHWs) have increased in frequency2. Here we quantify trends and attributes of MHWs across all ocean basins and examine their biological impacts from species to ecosystems. Multiple regions in the Pacific, Atlantic and Indian Oceans are particularly vulnerable to MHW intensification, due to the co-existence of high levels of biodiversity, a prevalence of species found at their warm range edges or concurrent non-climatic human impacts. The physical attributes of prominent MHWs varied considerably, but all had deleterious impacts across a range of biological processes and taxa, including critical foundation species (corals, seagrasses and kelps). MHWs, which will probably intensify with anthropogenic climate change3, are rapidly emerging as forceful agents of disturbance with the capacity to restructure entire ecosystems and disrupt the provision of ecological goods and services in coming decades.Marine heatwaves are increasing in frequency, but they vary in their manifestation. All events impact ecosystem structure and functioning, with increased risk of negative impacts linked to greater biodiversity, number of species near their thermal limit and additional human impacts.

Several aspects of regional climate including near-surface temperature and precipitation are predictable on interannual to decadal time scales. Despite indications that some climate states may provide higher predictability than others, previous studies analysing decadal predictions typically sample a variety of initial conditions. Here we assess multi-year predictability conditional on the phase of the El Niño–Southern Oscillation (ENSO) at the time of prediction initialisation. We find that predictions starting with El Niño or La Niña conditions exhibit higher skill in predicting near-surface air temperature and precipitation multiple years in advance, compared to predictions initialised from neutral ENSO conditions. This holds true in idealised prediction experiments with the Community Climate System Model Version 4 and to a lesser extent also real-world predictions using the Community Earth System Model and a multi-model ensemble of hindcasts contributed to the Coupled Model Intercomparison Project Phase 6 Decadal Climate Prediction Project. This enhanced predictability following ENSO events is related to phase transitions as part of the ENSO cycle, and related global teleconnections. Our results indicate that certain initial states provide increased predictability, revealing windows of opportunity for more skillful multi-year predictions. The study identifies windows of opportunity for multi-year climate predictions, depending on the state of ENSO. Predictions started during El Niño and La Niña exhibit higher skill than predictions started during neutral ENSO conditions.

This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. Two complementary statistical techniques were adopted to evaluate the possible nonstationary behavior of these precipitation data. The first was a Mann–Kendall nonparametric trend test, and it was used to evaluate the existence of monotonic trends. The second was a nonstationary generalized extreme value analysis, and it was used to determine the strength of association between the precipitation extremes and globally averaged near-surface temperature. The outcomes are that statistically significant increasing trends can be detected at the global scale, with close to two-thirds of stations showing increases. Furthermore, there is a statistically significant association with globally averaged near-surface temperature, with the median intensity of extreme precipitation changing in proportion with changes in global mean temperature at a rate of between 5.9% and 7.7% K−1, depending on the method of analysis. This ratio was robust irrespective of record length or time period considered and was not strongly biased by the uneven global coverage of precipitation data. Finally, there is a distinct meridional variation, with the greatest sensitivity occurring in the tropics and higher latitudes and the minima around 13°S and 11°N. The greatest uncertainty was near the equator because of the limited number of sufficiently long precipitation records, and there remains an urgent need to improve data collection in this region to better constrain future changes in tropical precipitation.

All cultivars tested to date are diploid or triploid and triploid have thicker stems, larger flowers, and larger stoma compared to related diploids. It is unknown whether interploidy crosses between diploid and triploid hydrangeas can be used to develop triploid varieties. The objective of this study was to compare pollen tube development, fruit formation, and seed viability among intra- and interploidy pollinations of and evaluate the genome size and pollen viability of resultant progeny. By 24 h post-pollination, pollen tubes had reached the ovaries of diploid flowers in 48.7% of samples while pollen tubes reached the ovaries in only 8.7% of triploid flowers ( = 30.6, < 0.001). By 48 h post-pollination pollen tubes reached the ovaries of diploid and triploid flowers in 72.5% and 53.8% of samples, respectively ( = 26.5, = 0.001). There was no difference in percentage of flowers with pollen tubes reaching the ovaries in diploid and triploid flowers at 72 h after pollination ( = 7.5, = 0.60). Analysis of covariance showed that pollen tube length at 24 and 48 h post-pollination was significantly influenced by ploidy and flower length of the female parent. Progeny of interploidy crosses was diploid and aneuploid; no triploid progeny were recovered from crosses using triploid parents. Mean genome sizes of offspring from each cross type ranged from 4.56 pg for 2x × 2x offspring to 5.17 pg for 3x × 3x offspring. Estimated ploidy of offspring ranged from 2x for 2x × 2x crosses to 2.4x for 3x × 3x crosses. Pollen stainability rates of flowering offspring using a modified Alexander's stain ranged from 69.6% to 76.4%.

A range of in situ, satellite and reanalysis products on a common daily 1° × 1° latitude/longitude grid were extracted from the Frequent Rainfall Observations on Grids database to help facilitate intercomparison and analysis of precipitation extremes on a global scale. 22 products met the criteria for this analysis, namely that daily data were available over global land areas from 50°S to 50°N since at least 2001. From these daily gridded data, 10 annual indices that represent aspects of extreme precipitation frequency, duration and intensity were calculated. Results were analysed for individual products and also for four cluster types: (i) in situ, (ii) corrected satellite, (iii) uncorrected satellite and (iv) reanalyses. Climatologies based on a common 13-year period (2001-2013) showed substantial differences between some products. Timeseries (which ranged from 13 years to 67 years) also highlighted some substantial differences between products. A coefficient of variation showed that the in situ products were most similar to each other while reanalysis products had the largest variations. Reanalyses however agreed better with in situ observations over extra-tropical land areas compared to the satellite clusters, although reanalysis products tended to fall into 'wet' and 'dry' camps overall. Some indices were more robust than others across products with daily precipitation intensity showing the least variation between products and days above 20 mm showing the largest variation. In general, the results of this study show that global space-based precipitation products show the potential for climate scale analyses of extremes. While we recommend caution for all products dependent on their intended application, this particularly applies to reanalyses which show the most divergence across results.

Using a global observational dataset of daily gridded maximum and minimum temperatures we investigate changes in the respective probability density functions of both variables using two 30‐year periods; 1951–1980 and 1981–2010. The results indicate that the distributions of both daily maximum and minimum temperatures have significantly shifted towards higher values in the latter period compared to the earlier period in almost all regions, whereas changes in variance are spatially heterogeneous and mostly less significant. However asymmetry appears to have decreased but is altered in such a way that it has become skewed towards the hotter part of the distribution. Changes are greater for daily minimum (night‐time) temperatures than for daily maximum (daytime) temperatures. As expected, these changes have had the greatest impact on the extremes of the distribution and we conclude that the distribution of global daily temperatures has indeed become “more extreme” since the middle of the 20th century. Key Points Recent observed warming is investigated using global PDFs PDFs systematically shift and skew towards the hotter part of the distribution In most regions extreme temperatures increased more than mean temperatures