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A growing body of research shows that extreme climatic events, e.g. heatwave, rainstorms and droughts, are becoming more frequent and intensified across various regions of the world. Australia is not isolated from these changes with marked increase in both rainfall and temperature extremes. Inherently, we understand that exposure to these extreme events could encourage decisions about population flow, and quantifying this linkage is challenging, especially for communities in small areas with an average of 10,000 people. Using spatio-temporal statistical techniques, this paper examines the possible environmental and socio-economic drivers associated with population flows of small communities as well as the possible predictive scenarios due to the effects introduced by climatic extremes. The analysis has been undertaken for a case-study region in the Murray-Darling Basin, Australia, where the economy is underpinned by agriculture and is sensitive to climate variability and extremes. The analysis reveals that in addition to the socio-economic factors, the environmental variables have a statistically significant association on shaping the distribution of the population flows in the study area. This statistical analysis can direct further data collection and causality analysis and be beneficial for policy makers, stakeholders and local communities to work together to adapt the Basin to climate extremes and changes. [ABSTRACT FROM AUTHOR]

Droughts have adverse socioeconomic, agricultural, and environmental impacts that can be reduced by assessing and forecasting drought behavior. The paper presents detailed analyses of both meteorological and vegetative droughts over the period from 1970 to 2005. Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) have been used to quantify drought according to severity, magnitude and spatial distribution at the Hashemite Kingdom of Jordan. Results suggest that the country faced during the past 35 years frequent non-uniform drought periods in an irregular repetitive manner. Drought severity, magnitudes and life span increased with time from normal to extreme levels especially at last decade reaching magnitudes of more than 4. Generated NDVI maps spatial analyses estimate crop-area percentage damage due to severe and extremely severe drought events occurred during October, December, and February of 2000 to be about 10%, 45%, and 30%, respectively. In response to drought spatial extent, the paper suggest the presence of two drought types, local drought acting on one or more geographical climatic parts and national drought, of less common but more severe, that extend over the whole country. Droughts in Jordan act intensively during January, February and March and tend to shift position with time by alternative migrations from southern desert parts to northern desert parts and from the eastern desert parts to highlands and Jordan Rift Valley (JRV) at the west. The paper also investigates the potential use of Global Climate Model's (GCM) to forecast future drought events from 2010 till 2040. Tukey HSD test indicates that ECHAM5OM GCM is capable to predicted rainfall variation at the country and suggests future droughts to become more intensive at the northern and southern desserts with 15% rainfall reduction factor, followed by 10% reduction at the JRV, and 5% at the highlands. [ABSTRACT FROM AUTHOR]

Climate change has the potential ability to alter the occurrence and severity of extreme events. Though predicting changes of such extreme events is difficult, understanding them is important to determine the impacts of climate change in various sectors. This paper presents the change in rainfall extremes in the monsoon season in south-west Indian peninsula. Daily rainfall data were analysed for the entire Kerala state in India to determine if the extreme rainfall had changed over the 50-year period. Several indices were derived from the data to identify the extreme rainfalls. The trends of all the extreme indices were assessed by parametric ordinary least square regression technique, which were tested for significance at 95% level. Results showed significant decrease in monsoon rainfall extremes in Kerala that would affect the tendency of change in seasonal total rainfall. This study provides a comprehensive knowledge on extreme monsoon precipitation in Kerala, which could also be employed to study changing climate at local scale in other regions. [ABSTRACT FROM AUTHOR]

This paper compares precipitation, maximum and minimum air temperature and solar radiation estimates from the Hadley Centre's HadRM3 regional climate model (RCM), (50x50 km grid cells), with observed data from 15 meteorological station in the UK, for the period 1960-90. The aim was to investigate how well the HadRM3 is able to represent weather characteristics for a historical period (hindcast) for which validation data exist. The rationale was to determine if the HadRM3 data contain systematic errors and to investigate how suitable the data are for use in climate change impact studies at particular locations. Comparing modelled and observed data helps assess and quantify the uncertainty introduced to climate impact studies. The results show that the model performs very well for some locations and weather variable combinations, but poorly for others. Maximum temperature estimations are generally good, but minimum temperature is overestimated and extreme cold events are not represented well. For precipitation, the model produces too many small events leading to a serious under estimation of the number of dry days (zero precipitation), whilst also over- or underestimating the mean annual total. Estimates represent well the temporal distribution of precipitation events. The model systematically over-estimates solar radiation, but does produce good quality estimates at some locations. It is concluded that the HadRM3 data are unsuitable for detailed (i.e. daily time step simulation model based) site-specific impacts studies in their current form. However, the close similarity between modelled and observed data for the historical case raises the potential for using simple adjustment methods and applying these to future projection data. [ABSTRACT FROM AUTHOR]

This paper builds on existing theory and proposes a framework to identify vulnerability to climate change in food systems by examining historic cases where common environmental problems caused famine. Cases presented are (1) Ireland's Potato Famine, (2) El Niño induced famines during the Colonial period, and (3) Ethiopia between 1965 and 1997. Three factors stand out as common in each. Prior to each famine: (1) there were very few ways that people could obtain a living in the worst affected regions; (2) livelihoods in famine stricken communities came to depend on highly specialized agro-ecosystems that were sensitive to environmental change; (3) institutions failed to provide adequate safety nets to protect livelihoods from failure. This analysis suggests that vulnerability to climate change in food systems can be assessed by looking at agro-ecosystems, livelihoods and institutions. Local conditions, however, mean that ways of measuring these three factors will vary from place to place. As a result, direct comparisons are difficult. By conceptualizing these three variables as the axes of a three dimensional "vulnerability" space, it is possible to compare regions and look at trends over time by studying the paths through this "space" as traced by changes at the agroecosystem, livelihood, and institutional scale. [ABSTRACT FROM AUTHOR]

In this paper, unpublished archival documentary sources are used to explore the vulnerability to--and implications of--climatic variability and extreme weather events in colonial Mexico. Attention focuses on three regions covering a variety of environmental, social, economic, and political contexts and histories and located at key points along a north-south rainfall gradient: Chihuahua in the arid north, Oaxaca in the wetter south and Guanajuato located in the central Mexican highlands. A number of themes are considered. First, the significance of successive, prolonged, or combined climate events as triggers of agrarian crisis. Second, a case study demonstrating the national and regional impacts of a particularly devastating climate induced famine, culminating with the so-called 'Year of Hunger' between 1785 and 1786, is presented. The way in which social networks and community engagement were rallied as a means of fortifying social resilience to this and other crises will be highlighted. Third, the impacts of selected historical flood events are explored in order to highlight how the degree of impact of a flood was a function of public expectation, preparedness and also the particular socio-economic and environmental context in which the event took place. An overview of the spatial and temporal variations in vulnerability and resilience to climatic variability and extreme weather events in colonial Mexico is then provided, considering those recorded events that could potentially relate to broader scale, possibly global, climate changes. [ABSTRACT FROM AUTHOR]

The article presents an analysis concerning the possible impact of climate change on the frequency of absolute extreme events in England. It notes that the effect of climate change has been dismissed by some writers because of the belief that expected rates of future changes lie within those experienced in the past. Moreover, it outlines the use of an appropriate time-dependent climate model as practical way to tackling the lag problem.

It is well known that temperatures across the globe are rising, but climatic conditions are becoming more variable as well. Forecasts of species range shifts, however, often focus on average climatic changes while ignoring increasing climatic variability. In particular, many species distribution models use space-for-time substitution, which focuses exclusively on the effect of average climatic conditions on the target species across a geographic range, and is blind to the possibility of range-wide population collapse with increasing drought frequency, drought severity, or climate effects on other co-occurring species. Relegated to assessments of broad demographic patterns that ignore underlying biological responses to increasing climatic variability, this prevalent method of distribution forecasting may systematically underpredict climate change impacts. We compare six models of survival and abundance of a subcanopy tree species, Taxus brevifolia, over 40 years of past climate change to disentangle multiple sources of uncertainty: model formulation, scale of climate effect, and level of biological organization. We show that drought extremes increased Taxus individual- and population-scale mortality across a wide geographic climate gradient, precluding detection of a monotonic relationship with average climate. Individual-scale climatic extremes models derived from longitudinal data had the highest predictive accuracy (82%), whereas mean climate models had the lowest accuracy (< 65%). Our results highlight that conclusions drawn from forecasts of average warming alone likely underpredict climate change impacts by ignoring indicators of range-wide population declines for species sensitive to increasing climatic variability. [ABSTRACT FROM AUTHOR]

Climate change will significantly impact the future viability and security of food production systems, with increased frequency and intensity of droughts, floods, storms and other extreme climatic events predicted in many regions. In order for food production systems to remain viable and resilient under a changing climate, novel approaches, which integrate risk management (i.e. adaptation) and risk transfer strategies, such as insurance, are required. We argue that the coordinated integration of risk management and risk transfer approaches will support greater resilience of food production systems under climate change. Conversely, if risk management and risk transfer strategies are not carefully integrated, there is potential to undermine adaptive capacity (e.g. insurance subsidies may dissuade farmers from investing in climate adaptation) and ultimately reduce the capacity of food production systems to cope with and recover from the adverse impacts of climate change. Here we propose a resilience-based conceptual framework for integrating risk management and risk transfer strategies along with four key principles, which we believe could underlie their successful integration and thus enhance food production system resilience under climate change. These are as follows: (1) pro-active investments in farmer climate adaptation rather than re-active disaster relief, (2) structuring of government subsidies around insurance and climate disaster relief to incentivise farmer climate adaptation, (3) rewarding farmer efforts towards climate adaptation with cheaper insurance premiums for those farmers that invest resources into climate adaptation and (4) recognising investments in the integration of farm climate adaptation and risk transfer schemes within the broader context of future climate disaster risk management and global food security. Such an integrated investment approach could substantially reduce future economic losses for farmers while also enhancing food security under climate change. [ABSTRACT FROM AUTHOR]

The 2012 Midwestern US drought resulted in major impacts to farmers and the public. Extreme events like drought will continue in the future due to climate change. We studied how the elite national press—New York Times (NYT) and Wall Street Journal (WSJ)—covered the 2012 Midwestern drought, how climate change was addressed, and how NYT and WSJ articles differed from each other and agricultural trade publication (ATP) article coverage before, during, and after the drought. A previous study found that ATP articles emphasized short-term drought recovery efforts. We suggest this emphasis was exacerbated by how drought and climate change were reported in the national media. Few articles discussed climate change in all publication types. Most articles that did cover climate change did not attribute a cause; however, over half of NYT articles mentioned human-caused climate change. WSJ and ATP articles had more content similarities than NYT articles. Overall, climate change discussions in all publication types were related to personal impacts. Climate change reporting in the WSJ and NYT was broad. ATP climate change reporting related to weather conditions in relation to farm resilience. Focusing on impacts and recovery may have attenuated risk perceptions. We contend that communication should recognize human-caused climate change and increased likelihood of weather extremes. [ABSTRACT FROM AUTHOR]

A set of 28 simulations from five regional climate models are used in this study to assess the Great Lakes' water supply from 1953 to 2100 following emissions scenarios RCP4.5 and 8.5 with a focus on bi-weekly changes in the means and extremes of hydrological variables. Models are first evaluated by comparing annual cycles of precipitation, runoff, evaporation and net basin supply (NBS) with observations. Trends in mean values are then studied for each variable using Theil-Sen's statistical test. Changes in extreme conditions are analyzed using generalized extreme values distributions for a reference period (1971–2000) and two future periods (2041–2070 and 2071–2100). Ensemble trend results show evaporation increases of 136 and 204 mm (RCP4.5 and RCP8.5) over the Great Lakes between 1953 and 2100. Precipitation increases by 83 and 140 mm and runoff increases by 68 and 135 mm. Trends are not equally distributed throughout the year as seasonal changes differ greatly. As a result, Great Lakes net basin supply is expected to increase in winter and spring and decrease in summer. Over the entire year, NBS increases of 14 and 70 mm are projected for scenarios RCP4.5 and 8.5 respectively by the year 2100. An analysis of extreme values reveals that precipitation and NBS maxima increase by 11 to 27% and 1 to 9% respectively, while NBS minima decrease by 18 to 29% between 1971–2000 and 2041–2100. [ABSTRACT FROM AUTHOR]

The precipitation record taken by J.B. Beccari in Bologna from 1723 to 1765, with three observations a day, has been recovered from the original Logs and analysed. Although metadata are scarce or even missing, a thorough investigation of the contemporary sources, the interactions between Beccari, his colleagues in Padua, and the Royal Society London, as well as the record analysis, have allowed reconstruction of most of the information concerning the instrument, its location and exposure, the measuring protocol and units. Daily, monthly and yearly amounts and frequencies, as well as extreme events, have been analysed. The first decade (i.e. 1723–1733) was drier than the 1961–1990 reference period and the subsequent decades wetter. During the calendar year, summer was dryer and October was characterized by a stronger activity of Atlantic perturbations. [ABSTRACT FROM AUTHOR]

Despite broad scientific consensus on climate change, public views may not always correspond with scientific findings. Understanding public perceptions of climate change is thus crucial to both identifying problems and delivering solutions. Investigations of climate change that integrate instrumental records and people's perceptions in the Himalayas are scarce and fragmentary compared to other regions of the world. We analyzed nationally representative data (n = 5060) of local peoples' perception of climate change in Nepal, and assessed annual and seasonal trends of temperature and precipitation, onsets of seasons, and trends of climate extremes, based on gridded climate datasets. We firstly used quantitative and spatial techniques to compare local perceptions and the instrumentally observed trends of climate variables. We then examined the possible association of demographic variables, place attachment, regional differences, and prior understanding of climate change with the accuracy of people's perceptions. Instrumental evidence showed consistent warming, increasing hot days and nights, and increasing annual precipitation, wet spells, heavy precipitation and decreasing dry spells in Nepal. Our results indicate that locals accurately perceived the shifts in temperature but their perceptions of precipitation change did not converge with the instrumental records. We suggest that, in future as exposure to changes in weather, particularly extreme events, continues, people may become more likely to detect change which corresponds with observed trends. With some new methodological insights gained through integrating community perceptions with observed climate data, the results of this study provides valuable information to support policies to reduce climate-related risk and enhance climate change adaptation. [ABSTRACT FROM AUTHOR]

Climate data obtained from global climate models (GCMs) form the basis of most studies of regional climate change and its impacts. Using the northeastern U.S. as a test case, we develop a framework to systematically sub-select reliable models for use in climate change studies in the region. Model performance over the historical period is evaluated first for a wide variety of standard and process metrics including large-scale atmospheric circulation features that drive regional climate variability. The inclusion of process-based metrics allows identification of credible models in capturing key processes relevant for the climate of the northeastern U.S. Model performance is then used in conjunction with the assessment of redundancy in model projections, especially in summer precipitation, to eliminate models that have better performing counterparts. Finally, we retain some mixed-performing models to maintain the range of climate model uncertainty, required by the fact that model biases are not strongly related to their respective projections. This framework leads to the retention of 16 of 36 CMIP5 GCMs that (a) have a satisfactory historical performance for a variety of metrics and (b) provide diverse climate projections consistent with uncertainties in the multi-model ensemble (MME). Overall, the models show significant variations in their performance across metrics and seasons with none emerging as the best model in all metrics. The retained set reduces the number of models by more than one half, easing the computational burden of using the entire CMIP5 MME, while still maintaining a wide range of projections for risk assessment. The retention of some mixed-performing models to maintain ensemble uncertainty suggests a potential to narrow the ranges in temperature and precipitation. But any further refinement should be based on a more detailed analysis of models in capturing regional climate variability and extremes to avoid providing overconfident projections. [ABSTRACT FROM AUTHOR]

Extreme weather events are projected to be more frequent and severe across the globe because of global warming. This poses challenging problems for critical infrastructures, which could be dramatically affected (or disrupted), and may require adaptation plans to the changing climate conditions. The INTACT FP7-European project evaluated the resilience and vulnerability of critical infrastructures to extreme weather events in a climate change scenario. To identify changes in the hazard induced by climate change, appropriate extreme weather indicators (EWIs), as proxies of the main atmospheric features triggering events with high impact on the infrastructures, were defined for a number of case studies and different approaches were analyzed to obtain local climate projections. We considered the influence of weighting and bias correction schemes on the delta approach followed to obtain the resulting projections, considering data from the Euro-CORDEX ensemble of regional future climate scenarios over Europe. The aim is to provide practitioners, decision-makers, and administrators with appropriate methods to obtain actionable and plausible results on local/regional future climate scenarios. Our results show a small sensitivity to the weighting approach and a large sensitivity to bias correcting the future projections. [ABSTRACT FROM AUTHOR]

Crop yields are vulnerable to climate change. We assess the global impacts of climate change on agricultural systems under two climate projections (RCP8.5 and RCP4.5) to quantify the difference in impacts if climate change were reduced. We also employ two different socioeconomic pathways (SSP3 and SSP5) to assess the sensitivity of results to the underlying socioeconomic conditions. The integrated-Population-Economy-Technology-Science (iPETS) model, a global integrated assessment model for projecting future energy use, land use and emissions, is used in conjunction with the Community Earth System Model (CESM), and particularly its land surface component, the Community Land Model (CLM), to evaluate climate change impacts on agriculture. iPETS results are produced at the level of nine world regions for the period 2005–2100. We employ climate impacts on crop yield derived from CLM, driven by CESM simulations of the two RCPs. These yield effects are applied within iPETS, imposed on baseline and mitigation scenarios for SSP3 and SSP5 that are consistent with the RCPs. We find that the reduced level of warming in RCP4.5 (relative to RCP8.5) can have either positive or negative effects on the economy since crop yield either increases or decreases with climate change depending on assumptions about CO2 fertilization. Yields are up to 12 % lower, and crop prices are up to 15 % higher, in RCP4.5 relative to RCP8.5 if CO2 fertilization is included, whereas yields are up to 22 % higher, and crop prices up to 22 % lower, if it is not. We also find that in the mitigation scenarios (RCP4.5), crop prices are substantially affected by mitigation actions as well as by climate impacts. For the scenarios we evaluated, the development pathway (SSP3 vs SSP5) has a larger impact on outcomes than climate (RCP4.5 vs RCP8.5), by a factor of 3 for crop prices, 11 for total cropland use, and 35 for GDP on global average. [ABSTRACT FROM AUTHOR]

Extreme heat is a significant public health challenge in urban environments that disproportionally impacts vulnerable members of society. In this research, demographic, economic and climate projections are brought together with a statistical approach linking extreme heat and mortality in Houston, Texas. The sensitivity of heat-related non-accidental mortality to future changes of demographics, income and climate is explored. We compare climate change outcomes associated with two different Representative Concentration Pathways (RCPs), RCP4.5 and RCP8.5, which describe alternate future scenarios for greenhouse gas emissions and concentrations. For each RCP, we explore demographic and economic scenarios for two plausible Shared Socioeconomic Pathways (SSPs), SSP3 and SSP5. Our findings suggest that non-accidental mortality in 2061–2080 may increase for all combinations of RCP and SSP scenarios compared to a historical reference period spanning 1991–2010. Notably, increased heat-related non-accidental mortality is associated with changes in the size and age of the population, but the degree of sensitivity is highly uncertain given the breadth of plausible socioeconomic scenarios. Beyond socioeconomic changes, climate change is also important. For each socioeconomic scenario, non-accidental mortality associated with the lower emissions RCP4.5 scenario is projected to be 50 % less than mortality projected under the higher emissions RCP8.5 scenario. [ABSTRACT FROM AUTHOR]

We investigate simulated hydrological extremes (i.e., high and low flows) under the present and future climatic conditions for five river basins worldwide: the Ganges, Lena, Niger, Rhine, and Tagus. Future projections are based on five GCMs and four emission scenarios. We analyse results from the HYPE, mHM, SWIM, VIC and WaterGAP3 hydrological models calibrated and validated to simulate each river. The use of different impact models and future projections allows for an assessment of the uncertainty of future impacts. The analysis of extremes is conducted for four different time horizons: reference (1981-2010), early-century (2006-2035), mid-century (2036-2065) and end-century (2070-2099). In addition, Sen's non-parametric estimator of slope is used to calculate the magnitude of trend in extremes, whose statistical significance is assessed by the Mann-Kendall test. Overall, the impact of climate change is more severe at the end of the century and particularly in dry regions. High flows are generally sensitive to changes in precipitation, however sensitivity varies between the basins. Finally, results show that conclusions in climate change impact studies can be highly influenced by uncertainty both in the climate and impact models, whilst the sensitivity to climate modelling uncertainty becoming greater than hydrological model uncertainty in the dry regions. [ABSTRACT FROM AUTHOR]

Extreme weather events, at least in the short term, will arguably cause more damage and thus adversely affect society more than long term changes in the mean climate that are attributed to anthropogenic greenhouse gas emissions. While it was long perceived as impossible to directly link a singular event with external climate drivers the emerging science of probabilistic event attribution renders it possible to attribute the fraction of risk caused by anthropogenic climate change to particular weather events and their associated losses. The robust link of only a small fraction of excessive deaths in, e.g., a heatwave to manmade climate change is very significant from an ethical point of view and we argue that this has widespread implications, e.g. for pending policy decisions concerning the Warsaw International Mechanism for Loss and Damage and the recognition of such losses in the broader context of climate justice. [ABSTRACT FROM AUTHOR]

In this study, we analyze changes in extreme temperature and precipitation over the US in a 60-member ensemble simulation of the 21st century with the Massachusetts Institute of Technology (MIT) Integrated Global System Model-Community Atmosphere Model (IGSM-CAM). Four values of climate sensitivity, three emissions scenarios and five initial conditions are considered. The results show a general intensification and an increase in the frequency of extreme hot temperatures and extreme precipitation events over most of the US. Extreme cold temperatures are projected to decrease in intensity and frequency, especially over the northern parts of the US. This study displays a wide range of future changes in extreme events in the US, even simulated by a single climate model. Results clearly show that the choice of policy is the largest source of uncertainty in the magnitude of the changes. The impact of the climate sensitivity is largest for the unconstrained emissions scenario and the implementation of a stabilization scenario drastically reduces the changes in extremes, even for the highest climate sensitivity considered. Finally, simulations with different initial conditions show conspicuously different patterns and magnitudes of changes in extreme events, underlining the role of natural variability in projections of changes in extreme events. [ABSTRACT FROM AUTHOR]

In the next few decades, it is likely that California must face the challenge of coping with increased impacts from extreme events such as heat waves, wildfires, droughts, and floods. This study presents new projections of changes in the frequency and intensity of extreme events in the future across climate models, emissions scenarios, and downscaling methods, and for each California county. Consistent with other projections, this study finds significant increases in the frequency and magnitude of both high maximum and high minimum temperature extremes in many areas. For example, the frequency of extreme temperatures currently estimated to occur once every 100 years is projected to increase by at least ten-fold in many regions of California, even under a moderate emissions scenario. Under a higher emissions scenario, these temperatures are projected to occur close to annually in most regions. Also, consistent with other projections, analyses of precipitation extremes fail to detect a significant signal of change, with inconsistent behavior when comparing simulations across different GCMs and different downscaling methods. [ABSTRACT FROM AUTHOR]

The Royal Netherlands Meteorological Institute (KNMI) has published the KNMI'06 climate scenarios in 2006. These scenarios give the possible states of the climate in The Netherlands for the next century. Projections of changes in precipitation were made for a time scale of 1 day. The urban drainage sector is, however, more interested in projections on shorter time scales. Specifically, time scales of 1 h or less. The aim of this research is to provide projections of precipitation at these shorter time scales based on the available daily scenarios. This involves an analysis of climate variables and their relations to precipitation at different time scales. On the basis of this analysis, one can determine a numeric factor to translate daily projections into shorter time scale projections. [ABSTRACT FROM AUTHOR]

Projections of changes in climate extremes are critical to assessing the potential impacts of climate change on human and natural systems. Modeling advances now provide the opportunity of utilizing global general circulation models (GCMs) for projections of extreme temperature and precipitation indicators. We analyze historical and future simulations of ten such indicators as derived from an ensemble of 9 GCMs contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR4), under a range of emissions scenarios. Our focus is on the consensus from the GCM ensemble, in terms of direction and significance of the changes, at the global average and geographical scale. The climate extremes described by the ten indices range from heat-wave frequency to frost-day occurrence, from dry-spell length to heavy rainfall amounts. Historical trends generally agree with previous observational studies, providing a basic sense of reliability for the GCM simulations. Individual model projections for the 21st century across the three scenarios examined are in agreement in showing greater temperature extremes consistent with a warmer climate. For any specific temperature index, minor differences appear in the spatial distribution of the changes across models and across scenarios, while substantial differences appear in the relative magnitude of the trends under different emissions rates. Depictions of a wetter world and greater precipitation intensity emerge unequivocally in the global averages of most of the precipitation indices. However, consensus and significance are less strong when regional patterns are considered. This analysis provides a first overview of projected changes in climate extremes from the IPCC-AR4 model ensemble, and has significant implications with regard to climate projections for impact assessments. [ABSTRACT FROM AUTHOR]