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In recent years, numerous studies have investigated the relationship of extreme precipitation intensities with temperature derived from a binning method using present‐day weather data. The aim of this approach is that these dependencies, or so‐called apparent scaling rates, provide insights on how precipitation extremes could evolve in the future climate. In a recent paper, Bao et al. showed that there is a large discrepancy between the present‐day apparent scaling rates—showing a negative dependency of daily rainfall extremes on temperature for Darwin—and the climate change response showing a substantial increase in daily rainfall extremes. Their results provide compelling evidence that it may not be straightforward to predict the climate change response from the temperature scaling. We agree with this statement, but disagree with the physical explanation put forward in that study and think that moisture limitations are the most likely cause. Therefore, we advocate the use of dew point temperatures in scaling studies as a much more direct proxy of the absolute humidity of the air in which precipitation extremes develop, which we think is physically more consistent with the underlying mechanism causing the increase in precipitation extremes with global warming. [ABSTRACT FROM AUTHOR]

There is an urgent need to understand and predict how extreme precipitation events (EPEs) will change at high latitudes, both for local climate change adaptation plans and risk mitigation and as a potential proxy "anticipating" the impact of climate change elsewhere in the world. This paper illustrates that a combination of non‐stationary modelling approaches can be adopted to evaluate trends in EPEs under uncertainty. A large database of daily rainfall events from 281 sparsely distributed weather stations in Finland between 1961 and 2016 was analysed. Among the tested methods, Poisson distributions provided a powerful method to evaluate the impacts of multiple physical covariates, including temperature and atmospheric circulation patterns (ACPs), on the resulting trends. The analysis demonstrates that non‐stationarity is statistically valid for the majority of observations, independently of their location in the country and the season of the year. However, subsampling can severely hinder the statistical validity of the trends, which can be easily confused with random noise and therefore complicate the decision‐making processes regarding long‐term planning. Scaling effects have a strong impact on the estimates of non‐stationary parameters, as homogenizing the data in space and time reduces the statistical validity of the trends. Trends in EPE statistics (mean, 90 and 99% percentiles) and best‐fitted Generalized Pareto parameters in the tails of the distributions appear to be stronger when approaching the Polar region (Lapland) than away from it, consistent with the Arctic amplification of climate change. ACPs are key covariates in physically explaining these trends. In particular, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) can explain statistically significant increases in extreme precipitation in Lapland, Bothnian and South regions of Finland, particularly during summer and fall seasons. Non‐stationarity is statistically valid in the majority of observations but masked by subsampling and spatio‐temporal heterogeneity. Trends on extremes and non‐stationary generalized Pareto parameters stronger when approaching the Polar region (Lapland). Artic Oscillation (AO) and North Atlantic Oscillation (NAO) can explain statistically significant trends. [ABSTRACT FROM AUTHOR]

In the paper we focus on the notes of botanist and meteorologist Gottfried Reyger on the weather conditions in Gdańsk, Poland. In our estimation, this chronicle, covering the period from December 1721 to June 1786 (the longest weather chronicle recorded by a single person), is a reliable source of information with the potential to contribute to the understanding of climate variability in 18th‐century Gdańsk, the significant old Hanseatic city in northern Poland. The temperature and precipitation series for the period 1721–1786 were indexed against contemporary conditions using the calculated percentages of cold, normal and warm decades. The climate in the 18th century in Gdańsk was colder than nowadays; the springs were much cooler, as were the winters, while the summers were warmer. A few relatively cold periods and several warm periods were identified. The first half of the analysed period was very humid, whereas the second was quite dry. The reconstructed indexed data series were compared to annual and seasonal mean values of air temperature and precipitation derived from regular instrumental observations made in Gdańsk since 1739. The linear regression method was applied to calibrate the reconstructed index series against anomalies from the 1961–1990 average. The correlation coefficient, standard error of estimate and the root‐mean‐square error statistics were used to diagnose the quality of calibration and verification. The highest correlation between the temperature indices and the observational series was found for winter. The reconstructed series for precipitation has a smaller variability than in reality. The reconstructed values for the years prior to 1739 indicate cold years for 1725–1732 and a wet period in the 1730s. The chronicle of botanist and meteorologist Gottfried Reyger, covering the period 1721–1786, characterizes the thermal and precipitational conditions in Gdańsk, Poland. The series were indexed against contemporary conditions, compared with instrumental data and reconstructed for early non‐instrumental period. The 18th‐century Gdańsk was colder than nowadays, particularly on case of springs, while the summers were warmer. The first decades of the period were very humid, whereas the latter ones quite dry. In the figure, annual air temperature (left panel) and precipitation (right panel) reconstruction for Gdańsk for the period 1722–1785 presented in the context of observational series, 1739–1785 for air temperature and 1739–1772 for precipitation. Solid line shows observational data; dotted line shows reconstructed series. [ABSTRACT FROM AUTHOR]

Recent studies have focused on identifying the spatial coverage of heavy rainfall associated with tropical cyclones (TCs) and their remnants (TC‐events) throughout the southwestern United States from eastern North Pacific storms. Yet little is known about the larger spatial contribution of these storms among extreme floods over the western United States. This paper examines the spatial and fractional contribution of 103 TC‐events among annual maximum streamflow at 1,429 long‐term U.S. Geological Survey (USGS) streamgages with at least 30 years of daily discharge data over the 1958–2010 water years. The highest fractional contributions to annual daily maxima by these storms (~5–14%) are found in southern California, Arizona, southernmost Nevada and Utah, southern and western New Mexico, central Colorado, and Texas. While nearly 32% of the streamgages have at least one annual maximum that was generated by a TC‐event, TC‐events play a limited role in contributing to the upper tail of the flood peak distribution across the western United States. [ABSTRACT FROM AUTHOR]

In this paper we analyse the relationship between the mean and extreme precipitation for five stations in Hungary and the circulation types at the 500 hPa level, for a 52‐year period, 1958–2010. We used daily rainfall data for each station and a calendar of the daily circulation types at the 500 hPa geopotential height for Hungary based on an automatic classification scheme. A trend analysis showed that, in general, the mean and extreme precipitation rarely exhibit significant trends, similarly to previous studies. The only exception was found for the number of annual and summer rainy days in Budapest with significant negative trends (p = .05), as well as the number of days with rainfall amounts greater than the 95% probability level for Debrecen with a significant positive trend. It was found that the circulation types that are mainly responsible for the largest precipitation amounts overall show (in some cases significant) negative trends, as well as the precipitation amounts that are associated with them (Cwsw, Cse, Cwnw). In most cases, the circulation types that are prevailing during the extreme precipitation events are associated with western or almost‐western atmospheric circulations (Cwsw, Ase) and Mediterranean‐ or Atlantic‐origin depressions. [ABSTRACT FROM AUTHOR]

There are two aims of this paper: First, the relationship between daily and hourly precipitation extremes and air temperature is investigated for the validity of Clausius–Clapeyron (C–C) scaling; second, the impact of the atmospheric circulation and transport of humidity on the extreme precipitation events occurrence in Poland is analysed based on data obtained from 26 stations across Poland in the period 1951–2015. It was shown that daily‐scale extreme rainfall is less sensitive to changes in local atmospheric temperature but the hourly intensities reveal a similar behaviour at all stations showing a robust signal over the entire country. Regarding the impact of the large‐scale atmospheric circulation, three types were distinguished in cool part of the year: one connected with the positive phase of the North Atlantic Oscillation (NAO), second with a low‐pressure zone in the north and high‐pressure in the south of Europe and the northern Atlantic and third with a low over central Europe. Each time high precipitation in Poland was connected with the advection of humid air from the western sectors (SW, W or NW). In the warm part of the year three types were distinguished too, Poland was always in the scope of low‐pressure systems with the advection of warm air from the southern sectors or when the warm air from the south meets the colder air from the north. Similar localizations of low centres have been found in the case of local‐scale atmospheric circulation types during events with extreme precipitation. Both in cool and warm part of the year such events are connected with the increase of water vapour in the air. In cool season the wet and warm air masses inflow to Poland the most often from Atlantic, in the warm season—from Mediterranean and/or Black Seas. [ABSTRACT FROM AUTHOR]

The precipitation over arid regions may be low, but its environmental impacts can be very dramatic. The decadal variability in precipitation can also effectively disturb climatic environmental regimes. To advance the understanding of precipitation variability over arid regions, this paper focuses on decadal variability in summer precipitation over arid northwest China and its associated atmospheric circulations, using observation‐based data and atmospheric reanalysis during the period 1961–2016. The results show that climatologically, precipitation over this region has a strong seasonality and that 60.2% falls in summer. The summer precipitation increased 15.5 mm, accounting for 52.4% of the total annual increase in this part of China from 1961 to 2016. Decadal changes in summer precipitation occurred in approximately 1991 with a north–south difference structure modulated by large‐scale circulation anomalies over the Indian Ocean and central Asia as well as a shift in water vapour transport. In a positive phase, anticyclonic circulation anomalies occupy the Indian Ocean with cyclonic anomalies over central Asia, favouring stronger northwards moisture transport towards northwest China, and vice versa. Moreover, the significantly enhanced northwards water vapour transport from low‐latitude seas is also critical to the decadal changes approximately in 1991 summer precipitation in arid northwest China. These findings contribute to the understanding of decadal precipitation variability over arid regions, especially the increasing influence of low‐latitude circulations and moisture transport processes. [ABSTRACT FROM AUTHOR]

ABSTRACT: Extreme precipitation events are widely thought to intensify in a warmer atmosphere through the Clausius‐Clapeyron equation. The temperature‐extreme precipitation scaling was proposed to analyse the temperature dependency of short‐duration extreme precipitation and since then, the concept has been widely used in climatology. Bao et al. (2017) suggest that the apparent scaling reflects not only how surface air properties affect extreme precipitation, but also reflects how synoptic conditions and localized cooling due to the storm itself affect the scaling – implying two‐way causality. We address here critical issues of this paper and provide evidence that dew point temperature drives extreme precipitation, with the direction of causality reversed only for the storm's peak intensity. This physical inference may serve as a basis to better quantify scaling rates and to help establish the relationship between extreme precipitation and environmental conditions in the current climate, and thereby provide insights into future changes to precipitation extremes due to climate change. [ABSTRACT FROM AUTHOR]

ABSTRACT: The estimated future changes of precipitation and precipitation‐related climate indices for the Carpathian Region are analysed in this paper. Eleven regional climate model (RCM) simulations from the ENSEMBLES project taking into account the intermediate A1B emission scenario are used, hence, the analysis also includes uncertainty assessments. Validation results showed that in the target area RCM simulations overestimate precipitation throughout the year, except in summer, when underestimation occurs. In order to eliminate these systematic errors before the detailed analysis, percentile‐based bias correction is applied to the raw RCM outputs, for which the high‐quality CARPATCLIM database (1961–2010) serves as a reference. Using the bias‐corrected RCM simulations, precipitation totals and several precipitation‐related indices (describing both wet and dry climatic conditions) are calculated for the 1961–2100 time period on seasonal scale for the entire Carpathian Region, and also for five subregions (Slovakia, Ukraine, Hungary, Romania, and Serbia) within this area. The results from RCM simulations are inconsistent for some indices in some seasons, thus introducing uncertainties to the climatic projections. However, according to our results, drier summers are very likely to occur in the future, mainly in the southern parts of the selected domain. More frequent and more intense extreme precipitation events are estimated for winter and autumn, especially in the northern regions. Our conclusions suggest that the spatial distribution of precipitation is not likely to change remarkably in the future, however, the annual distribution of precipitation is projected to be restructured. [ABSTRACT FROM AUTHOR]

In a changing climate, it is likely that precipitation patterns will experience shifts in amount and seasonality, with a tendency towards increased drought in many parts of Europe. In order to investigate what could be some of the feedbacks of dry conditions on temperature, this paper has investigated close to 30 European locations to assess the influence of observed dry summer days on daily maximum temperature ( Tmax) as well as a clustering of rainless days, and finally the temperature impact related to drought conditions as defined by the Standardized Precipitation Index. The results show that for the three main European climatic regions investigated ('Mediterranean', 'Maritime', 'Continental'), the influence of rainless days is discernible, with an average difference between mean summer Tmax and Tmax during rainless days only of up to 1.5 °C. A further aim of the study is to determine whether recent shifts in the occurrence of rainless days during the summer have a bearing on temperature trends. Results show that shifts in rainless days explain only a fraction of the observed temperature change at the locations studied, thereby hinting that the enhanced greenhouse effect remains the dominant mechanism for temperature change. Finally, using a regional climate model, very preliminary insight into a 'greenhouse climate' highlights the fact that changes in dry conditions seem to confirm the findings for current climate, i.e. they account for only a fraction of the increase in temperatures. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT This paper documents the changes in climate extremes over Indonesia during the past three decades (1983-2012) based on a subset of extreme indices recommended by the Expert Team on Climate Change Detection and Indices ( ETCCDI). The extreme indices were calculated based on the quality controlled daily observational data (minimum and maximum temperature and precipitation) from 88 weather stations. Overall, we found significant and spatially coherent trends of warming in the temperature indices over Indonesia, consistent with other studies conducted at different countries within the Southeast Asia. The frequency of cool days ( TX10p) and cools nights ( TN10p) had decreased whereas warm days ( TX90p) and warm nights ( TN90p) were observed more frequently. Averaged over the country, the annual mean of daily maximum ( TXmean) and minimum temperature ( TNmean) had increased significantly by 0.18 and 0.30 °C decade−1, respectively. Other temperature indices also showed significant warming trends. In contrast, trends in the precipitation extremes indices were generally not significant and less spatially coherent. However, a tendency towards wetter conditions was observed, in agreement with the results at the global scale. The daily precipitation intensity ( SDII) had increased significantly over the country by 0.21 mm day−1 decade−1 during the period studied. At the regional scale, we observed a significant wetting trend in the annual highest daily amount ( RX1day) and the rainfall amount contributed by the extremely very wet days (R99p) in the northern part of the country. The wetting trends of a number of extreme precipitation indices were depicted prominently in December-January-February ( DJF) and/or March-April-May ( MAM) seasons, both at country and regional levels. However, for the southern region of Indonesia, a drying tendency was observed for June-July-August ( JJA), September-October-November ( SON) and MAM. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper revisits the strength and stationarity of linear statistical relationships between indices of monsoon rainfall derived from rain gauge data for three regions in tropical West Africa [West Sahel (WS), Central Sahel (CS), and Guinea Coast (GC)] and various indices describing remote variations in the ocean-atmosphere climate systems from 1921 to 2009. The results reveal that both the Atlantic Multi-decadal Oscillation ( AMO) and the Atlantic Meridional Mode ( AMM) show a positive linear correlation to Sahel rainfall. The percent variance explained ( PVE) ranges from 10 to 25%. The correlation stems from periods longer than 8 years for AMO, but AMM does show a correlation on interannual time scales for WS that was absent in the 1970s and 1980s. The PVE by El Niño Southern Oscillation ( ENSO) indices is, though statistically significant, on the order of 10% and found mainly on the interannual time scale. A strong and stable correlation with PVEs larger than 50% is found between the sea surface temperatures (SSTs) in the Atlantic 3 region ( ATL3, 0°-20°W, 3°S-3°N) and the GC rainfall. On the other hand, the correlation between ATL3 and WS rainfall changed from significantly negative to significantly positive after the 1970s. The Eastern Mediterranean SSTs are found to be significantly related to CS rainfall, especially in recent years with PVEs between 36 and 47%. Multi-linear regression analyses reveal that the relative importance of the Indian Ocean is 42% in the optimal regression model. For the CS, this value is 37% for the Eastern Mediterranean SSTs and 70% in case of the GC using the ATL3 index. However, except for the GC, non-stationarities in the correlation between the climate state indexes and West African monsoon ( WAM) rainfall suggest the need of the application of different regression models depending on the active 'teleconnection regime'. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an analysis of winter climate variability based on daily mean temperature and precipitation data since 1970 in the Chic-Chocs Mountain range (located in the Gaspé Peninsula, Eastern Quebec, Canada). Mountain environments are particularly sensitive to rapid climate change and are therefore good indicators of recent global warming. The main goal of this study is to demonstrate how joint probability temperature/precipitation distributions can be used to estimate winter condition changes (trends) for six meteorological stations in the study area (the altitudinal range for the stations is from 5 to 574 m). The presence and persistence of snow cover was also estimated. Previous studies have shown a lack of evidence of significant trends in snow-cover characteristics (density, depth and snow water equivalent (SWE)) from the early 1980s to the present, despite an increase in temperature over the same period. A reanalysis of these data sets in addition to the use of a combination of temperature and precipitation data categorized into four modes (warm/wet, warm/dry, cold/wet and cold/dry) was also performed. Despite this new analysis, no clear evidence of climate change could be found in the study area over the last four decades. The results revealed that patterns and trends are quite different from one station to another, but when the environment is taken into account (valley or plateau, coastal versus inland) some apparent patterns emerge. [ABSTRACT FROM AUTHOR]

Empirical statistical downscaling has been widely used to produce finer-resolution climate data. This approach, in general, is derived from an implicit stationarity assumption. This paper aims at proving a statistical method that is fully applicable of generating daily precipitation in non-stationary conditions using historical station data. Daily records at five Oklahoma stations were split into calibration and validation periods. Linear relationships between transition probabilities of wet-following-wet ( P w/ w) and wet-following-dry ( P w/ d) days and mean monthly precipitation were established by connecting the two endpoints (one for the 30 driest months and another for the 30 wettest months of the calibration period) for each calendar month, and were then used to interpolate P w/ w and P w/ d for the validation period. The mean and standard deviation of daily precipitation were estimated using the mean and standard deviation of monthly precipitation of the validation periods as well as the interpolated P w/ w and P w/ d. The adjusted parameters were used to generate daily series using a weather generator. Statistics of the disaggregated daily precipitation amounts and frequency, as well as dry/wet spell sequence, agreed with those of the observed values of the validation periods reasonably well. The disaggregation method preserved statistics of monthly precipitation amounts extremely well, demonstrating the validity of the method for temporal disaggregation of non-stationary climate. The accuracy of the presented method is within the weather generator's expected performance. Overall, a straight line connecting the two endpoints, implicitly incorporating non-stationarity of climate states, is adequate for interpolating P w/ w and P w/ d to any climatic conditions within the entire range. However, a linear regression including data points in between would generally improve the P w/ w and P w/ d interpolations slightly if long records (e.g. > 60 years) are available for estimating the intermediate points. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

One way in which global climate model (GCM) output can be utilized to infer local impacts is through the use of synoptic climatology: creating a set of atmospheric patterns that capture the variability in the climate system, and then analyzing trends and variability in the frequency of these patterns moving into the future. In this paper, we demonstrate a new synoptic climatological technique for classifying atmospheric patterns that can be used in conjunction with GCM output data (in this case, the Community Climate System Model 3). We apply this method to 850-hPa temperature patterns over the contiguous United States to derive daily categorizations. A total of 15 clusters are created from the data set; once the mean GCM bias is removed, historical cluster frequencies in the GCM data set are not statistically different from those of the reanalysis data set. In the future, significant changes in frequency are observed across most of the transition season clusters, as they broaden in seasonality at the expense of winter clusters, some of which nearly entirely disappear. Changes are greater moving further into the future, and greater for the more carbon-intensive special report on emissions scenarios (SRES) scenarios (A1FI, A2) than the less-intensive scenario tested (B1). Diagnostics test how well the mean patterns of the reanalysis data set, GCM historical data set and the future GCM data sets resemble each other. For some clusters, mean bias between the historical and future data sets grows substantially by the end of the 21st century under the more carbon-intensive scenarios. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The study of the temporal-spatial variability of dry sequences and the probability of their occurrence are particularly important in understanding the impact of climate change on droughts. This paper analyses the different properties of dry sequences, focussing on extreme condition, analysing their degree of spatial coherence, and their temporal variability. For the study, daily precipitation data are used for the period 1961-2000 throughout Argentina. The region north of 40°S is divided in two from the meridian of 63°W with highly differentiated 'dry' properties. The eastern region is more homogeneous where mean dry sequences last less than six days and long sequences of about 60 days. The Andean region shows a marked east-west gradient in any of the above properties, with extreme values of over 10 days (mean sequences) and 150 days (the longest sequences). At the seasonal level the above properties reflect a differential pattern according to the time of year under study. In the case of summer, when the dry sequences can be more harmful to crops, the maximum duration in the Pampa region can extend to about 25 days. Owing to the problems that dry sequences of over 30 days can produce in the different ecosystems and based on the above findings, the temporal-spatial variability is analysed, and a variability over the years, together with a progressive decrease in the occurrence of such events, is found. The study of extreme dry events provides useful tools that can be applied to different hydrological and agricultural needs. Furthermore, our climatological findings help validate climate models. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Climate change is commonly evaluated as the difference between simulated climates under future and current forcings, based on the assumption that systematic errors in the current-climate simulation do not affect the climate-change signal. In this paper, we investigate the Canadian Regional Climate Model (CRCM) projected climate changes in the climatological means and extremes of selected basin-scale surface fields and its sensitivity to model errors for Fraser, Mackenzie, Yukon, Nelson, Churchill and Mississippi basins, covering the major climate regions in North America, using current (1961-1990) and future climate simulations (2041-2070; A2 and IS92a scenarios) performed with two versions of CRCM. Assessment of errors in both model versions suggests the presence of nonnegligible biases in the surface fields, due primarily to the internal dynamics and physics of the regional model and to the errors in the driving data at the boundaries. In general, results demonstrate that, in spite of the errors in the two model versions, the simulated climate-change signals associated with the long-term monthly climatology of various surface water balance components (such as precipitation, evaporation, snow water equivalent (SWE), runoff and soil moisture) are consistent in sign, but differ in magnitude. The same is found for projected changes to the low-flow characteristics (frequency, timing and return levels) studied here. High-flow characteristics, particularly the seasonal distribution and return levels, appear to be more sensitive to the model version. CRCM climate-change projections indicate an increase in the average annual precipitation for all basins except Mississippi, while annual runoff increases in Fraser, Mackenzie and Yukon basins. A decrease in runoff is projected for Mississippi. A significant decrease in snow cover is projected for all basins, with maximum decrease in Fraser. Significant changes are also noted in the frequency, timing and return levels for low flows. Copyright © 2006 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

The homogenization of climate observational series is a needed process before undertaking confidently any study of their internal variability, since changes in the observation methods or in the surroundings of the observatories, for instance, can introduce biases in the data of the same order of magnitude than the underlying climate variations and trends. Many methods have been proposed in the past to remove the unwanted perturbations from the climatic series, and some of them have been implemented in software packages freely available on the Internet. The Spanish project MULTITEST was intended to test their performance in an automatic way with synthetic monthly series of air temperature and atmospheric precipitation, to update inter‐comparison results from former projects, especially those of the COST Action ES0601. Several networks representing different climates and station densities were used to test a variety of homogenization packages on hundreds of random samples. Results were evaluated mainly in the form of root mean squared errors and errors in the trend of the series, showing that ACMANT, followed by Climatol, minimized these errors. However, other packages performed also relatively well, even outperforming them when there were simultaneous biases of the same sign in most or all the test series. [ABSTRACT FROM AUTHOR]

This paper analyses the daily precipitation data recorded for the last 75-80 years in SE Spain. Prior to statistical analysis, homogeneity tests were performed to determine the usefulness of the data (recorded in six pluviometric stations). If the data were homogeneous, the Mann–Kendall test was performed to detect monotonic trends for variables such as total precipitation, number of days with precipitation greater than 1, 20 or 40 mm, number of consecutive dry days and number of consecutive wet days. Moreover, the linear tendency was calculated and accepted (or rejected) by performing a Student's t-test of the calculated residual values. The results showed, on average, a clear change in precipitation patterns for the last 20 years. Annual rainfall had decreased, with values ranging from 0 to 15%, dry periods had increased in length, and the number of rainy days had decreased (especially when high daily precipitation was considered). This latter notion did not exactly concur with the conclusions of the Intergovernmental Panel on Climate Change (IPCC) 2014 Synthesis Report related to the study area. [ABSTRACT FROM AUTHOR]

ABSTRACT In a recent article, Diem (2012) examined the impact of the North Atlantic Subtropical High ( NASH) and atmospheric moistening on summer rainfall variability in the Atlanta, Georgia region. In his paper, he indicated that the results concerning the variability of the NASH western ridge discussed in Li et al. (2011) are incorrect. We present new evidence here to show that the comments by Diem (2012) are unjustified; the main conclusions in Li et al. (2011) were drawn according to the data over a 60-year (1948-2007) and 45-year (1958-2002) periods of NCEP/ NCAR and ERA-40 reanalysis, whereas conclusions of Diem (2011) were based upon the trend analysis of a similar but different index of the NASH using sub-periods of 1948-2009 (since late 1970s). The comment emphases the importance of climate dynamics to study precipitation variability over the Southeastern US and further strengthens the conclusions originally put forth in Li et al. (2011): over the last 60 years (1948-2007), the NASH has shown a significant trend of westward movement, and the meridional movement of the NASH western-ridge (i.e. its latitudinal change) has enhanced in the recent three decades. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper analyses the influence of different types of El Niño-Southern Oscillation ( ENSO) events on South American precipitation under a climate change scenario. We use two sub-selections of Coupled Model Intercomparison Project Phase 5 (CMIP5) models that have a good representation of teleconnections to the region, albeit with reduced magnitude anomalies. Comparing historical simulations and the RCP 8.5 scenario experiments, after removing the trend in the mean due climate change, the sub-ensemble means show similar global sea surface temperature anomalies and South American precipitation anomaly patterns in the future. No robust changes in the pattern or magnitude of precipitation anomalies are found, despite significant ENSO-related shifts in the Pacific basin, as highlighted in other studies. However, if the models are analysed individually, some show relatively strong changes in ENSO-related precipitation anomalies for both canonical and Modoki events. We show that, in individual models, tropical teleconnection changes associated with ENSO are primarily driven by changes in the magnitude of ENSO-related circulation but with no agreement on the sign. Increases in background moisture, which have been hypothesized to drive an intensification of ENSO-related precipitation anomalies, are of secondary importance. [ABSTRACT FROM AUTHOR]

ABSTRACT The temporal concentration of precipitation may be characterized using several methods. For climate-scale precipitation, concentration measures are usually performed by means of dimensionless indices such as the Gini index ( GI) or the Theil index. For the purposes of the present paper, a set of 66 409 daily time series from around the world were analysed to estimate the climatic concentration of precipitation. To this end, some of the most widely used indices were tested, i.e. the Theil index, the Gini index, the concentration index, the classic n index and an ordered version of the n index. Results show a strong connection between several indices, mainly between the GI and the ordered n index. The high correlation of these indices ( R = 0.98) reflects a theoretical connection between the shape and integration of the Lorenz curve. With regard to spatial distribution, the three main indices present the same relative areas of high and low concentration. The high temporal concentration of precipitation is generally linked to the rapid pace of physical processes such as convection in areas with a high degree of insolation and warm seas. The low temporal concentration of rainfall can be interpreted as a consequence of regular patterns (maritime flows or highly recurrent storms). A relationship between the number of rainy days and concentration indices was noted; however, their correlation depends on the region analysed. [ABSTRACT FROM AUTHOR]

This paper combines a wide‐area assessment of forecast changes in wintertime synoptic conditions over western North America with a meso‐scale alpine hydrometeorology model to evaluate the impacts of forecast climate change on snowpack conditions in an alpine watershed. The synoptic analysis was used to generate long‐term climate time series scenarios using the Canadian Centre for Climate Modelling and Analysis first‐generation coupled general circulation model (GCM). The alpine hydrometeorology model SIMGRID is used to predict changes in wintertime precipitation at the watershed scale. The SNOPAC model is a simple snow model that predicts the overall snow accumulation throughout a watershed based on the output from SIMGRID. A vapour transfer model has been incorporated into the SNOPAC model to estimate snow volumes more accurately. The model is applied to a small alpine watershed in the southern Canadian Rockies. The synoptic analysis and GCM output forecasts a modest increase in both winter precipitation and temperatures in the study area. The hypothesis herein is that the increase in winter precipitation due to synoptic conditions will not compensate for regional changes in the rain‐to‐snow ratios. The net result will be a decline in winter accumulations of precipitation as snow, and hence an expected decline in spring runoff. Copyright © 2005 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

This paper studies the temporal and spatial patterns of precipitation anomalies over southern central South America (SCSA; 22–40°S and 54–70°W), and their relationship with the sea-surface temperature (SST) variability over the surrounding tropical and subtropical Atlantic and Pacific Oceans. The data include monthly precipitation from 68 weather stations in central–northern Argentina and neighbouring Brazil, Paraguay and Uruguay, and monthly SSTs from the NOAA dataset with a 2° resolution for the period 1961–93. We use the method of canonical correlation analysis (CCA) to study the simultaneous relationship between bi-monthly precipitation and SST variability. Before applying the CCA procedure, standardized anomalies are calculated and a prefiltering is applied by means of an empirical orthogonal function (EOF) analysis. Thus, the CCA input consists of 10 EOF modes of SST and between 9 and 11 modes for precipitation and their corresponding principal components, which are the minimum number of modes necessary to explain at least 80% of the variance of the corresponding field. The results show that November–December presents the most robust association between the SST and SCSA precipitation variability, especially in northeastern Argentina and southern Brazil, followed by March–April and May–June. The period January–February, in contrast, displays a weak relationship with the oceans and represents a temporal minimum of oceanic influence during the summer semester. Based on the CCA maps, we identify the different oceanic and SCSA regions, the regional averages of SST and precipitation are calculated, and linear correlation analysis are conducted. The periods with greater association between the oceans and SCSA precipitation are November–December and May–June. During November–December, every selected region over SCSA reflects the influence of several oceanic regions, whereas during May–June only a few regions show a direct association with the oceans. The Pacific Ocean regions have a greater influence and are more widespread over SCSA; the Atlantic Ocean regions have an influence only over the northwestern and the southeastern parts of SCSA. In general, the relationship with the equatorial and tropical Atlantic and Pacific Oceans is of the type warm–wet/cold–dry, whereas the subtropical regions of both oceans show the opposite relationship, i.e. warm–dry/cold–wet. Copyright © 2004 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper describes a limited-area, reduced-process model of the large-scale physics of precipitation developed for predicting over climatic timescales. It represents an intermediate approach between a general circulation model (GCM) and full-physics model of precipitation at high resolutions. The model is designed for application in palaeoclimate modelling, specifically as input to ice-sheet models. The model is used to predict seasonal precipitation for the UK, using sea-surface temperatures, topography, mean wind speed and direction. Modelled data are compared with the United Kingdom Climate Impacts Programme (UKCIP) baseline climatologies for the UK and are shown, for westerly and southwesterly winds, to produce precipitation distributions and magnitudes which are in good agreement with the UKCIP data. The modelled data are much more spatially heterogeneous, reflecting the physical nature of the parameterization of the influence of orography. The model's sensitivity to temperature, wind speed and wind direction and its potential for driving ice-sheet models over long timescales are demonstrated. Copyright © 2000 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

When a correlation-based circulation pattern classification was applied to daily 700 hPa heights for 25 summer seasons, and the circulation types were related to surface temperature and precipitation for the Lake Superior basin, within-type variability was found to be high. Regarding temperature, the difference between warm and cold days of a type is a positive height anomaly centred to the east of the lake, which is very similar to the height anomaly for winter temperatures identified in an earlier study. The difference between wet and dry days of a type is a pattern quite unlike that for temperature. It is a dipole pattern consisting of a negative anomaly to the northwest of the lake and a positive anomaly to the east and southeast. These small-scale circulation components were missed by the correlation-based classification because they are much smaller than the differences in height between circulation types. Regression models, relating monthly mean temperature anomalies to circulation type frequencies, produced results comparable to those obtained for winter temperature. For precipitation, the variance explained by the regression models is only half that obtained for temperature. Two approaches were used to reduce within-type variability and to improve the regression results: the division of the first few circulation types into warm/cold and wet/dry subtypes on the basis of differences in 700 hPa vorticity (an approach introduced in the earlier paper), and a modified correlation-based circulation pattern classification. The modification consisted of raising the correlation threshold for a few selected grid-points to capture the small-scale circulation component and also, of lowering the threshold for the comparison between maps and map sectors in order to minimize the number of unclassified days. Of the two approaches used to reduce within-type variability, the modified classification scheme produced superior results. In particular, the variance in precipitation explained by the regression models is about 30% higher than that achieved using vorticity to create subtypes. Copyright © 2000 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper is part of a research programme devoted to the analysis of historical climate in Spain. A reconstruction of rainfall characteristics in Southern Spain from 1500 to the present is described. Weather information was taken from original documentary sources in the region. A numerical index was established to characterize the rainfall and its evolution. Results were calibrated with modern precipitation data and with the results of other studies of historical climate. The main objective is to obtain a long precipitation record. A preliminary analysis of the obtained series shows a fluctuating behavior with alternating dry and wet periods. The wettest periods occurred at the end of 16th century, the beginning of 17th century, and at the end of 19th century. The driest periods in the pre-instrumental era occurred during the first half of the 16th century, and around 1750. A general decreasing trend in precipitation can be observed from 1960 onwards. Copyright © 1999 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Interest in climatic change has never been greater and it is inevitable that the search for periodicities in the climatic record should be a popular means of attempting to unravel at least some of the complexities of the atmospheric system. This paper reviews aspects of the methodologies of spectral analysis and takes the example of the Barcelona precipitation series (1850–1991) to illustrate how both spectral analysis and other statistical analyses can provide information on climatic evolution as expressed through the medium of this one variable. Copyright © 1999 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The selection of general circulation models (GCMs) with high capability to represent the past and likely future climate for a specific geographical location is a crucial step to assess impacts of climate change on different sectors. This study included pool of 105 and 78 GCMs for representative concentration pathways (RCPs) 4.5 and 8.5, respectively, from Coupled Model Intercomparison Project—Phase 5, applied an advanced envelope‐based selection approach to select representative GCMs for the Koshi river basin in China and Nepal at short‐term (2016–2045), mid‐century (2036–2065) and end‐of‐century (2071–2100) periods, and developed range of possible future precipitation and temperature scenarios with high resolution downscaled data (10 × 10 km2), which is the novelty contribution of the study. Considering RCP4.5, average annual precipitation is expected to increase by 0–16%, 4–23% and 4–24% in the short‐term, mid‐century and end‐of‐century periods, respectively. Using RCP8.5, equivalent predictions are 6–20%, 6–36% and 13–49% in the short‐term, mid‐century and end‐of‐century periods, respectively. Average annual temperature is expected to increase, but with higher increases during winter than in the monsoon period. Considering RCP4.5, average annual temperature is expected to increase by 1–1.4°C, 1.3–1.9°C and 1.6–2.8°C in the short‐term, mid‐century and end‐of‐century periods, respectively. Similarly, using RCP8.5, equivalent predictions are 1–1.6°C, 1.8–2.9°C and 3.1–5.6°C in the short‐term, mid‐century and end‐of‐century periods, respectively. The ensemble mean of absolute change in average precipitation and temperature projects that High Himalaya and Tibet regions are more sensitive to climate change considering precipitation and temperature, respectively. The results also suggest that GCMs selection for a catchment varies with climate scenarios and specific future time periods. [ABSTRACT FROM AUTHOR]

Convection‐permitting models (CPMs) provide a better representation of sub‐daily precipitation statistics and convective processes on both climate and weather forecasting timescales. This study aims to establish what horizontal resolution and physical process settings are suitable for the first ensemble of convection‐permitting climate projections for the United Kingdom. For this purpose, 12‐year‐long simulations were run using three different convection‐permitting resolutions (grid spacing of 4 km, 2.2 km and 1.5 km) each with several configurations. The focus is on the ability to represent sub‐daily precipitation due to its potential for high impacts on society through flooding. The analysis shows that the use of a finer grid spacing within the convection‐permitting regime improves the representation of sub‐daily precipitation but the added value decreases as the grid becomes increasingly finer, while the computational costs substantially increase. Changes in the representation of physical processes can have as much impact as the grid spacing and can lead to similar differences in the diurnal cycle as between convection‐parametrized models and CPMs. Although the 4km model shows some key deficiencies that make it unreliable for climate projection, the 2.2km model performs as well as the 1.5km model for most of the metrics examined with considerable benefits in terms of computer cost. The optimal configuration and resolution identified here is being used for an ensemble of simulations run within the UK Climate Projections 2018 (UKCP18) project, which allows, for the first time, an estimate of uncertainty in future changes at convection‐permitting scale and providing more reliable climate change projections at local and hourly scales. [ABSTRACT FROM AUTHOR]

Results from the multiple‐realization, single‐model ensemble called "d4PDF" were used to generate hydroclimate indices for present and future climates over the Indochina region. The model performed reasonably well in capturing the reference precipitation characteristics of the present climate (1951–2010), in particular the seasonal pattern (e.g., JJA and SON) and intensity‐based indices such as SDII, R95p, Rx1D, and Rx3D. Under the +4 K future simulation conditions for 2051–2110, comprising six prescribed sea‐surface temperature scenarios, substantial and statistically significant increases in precipitation frequency (R10 and R20) and intensity (SDII, Rx1D, and Rx3D) were projected over the Indochinese Peninsula with a high degree of consistency. However, there were greater uncertainties in the changes significance of mean precipitation either during early dry‐season (DJF) or late monsoon (SON) indices demonstrated by results of the parametric U test in some river catchments, such as the Chao Phraya, Red River, central Mekong, lower Irrawaddy, and Salween Basins, where the projection consistencies in a positive change direction were less than 50%. Comparison of basin‐wide empirical probability distributions of precipitation indices pointed to a robust increase in the frequency of the occurrence of heavy precipitation in intensity indices (e.g., Rx1D, Rx3D, and SDII). The relationship between d4PDF precipitation and total runoff generation outputs from the land surface component was investigated. The results show a high degree of temporal correspondence across the region under the present climate in several seasonal and intensity indices, except during the dry period (DJF and MAM). Finally, we found that εP(P, R) of the annual and seasonal climatological indices of the future climate were always positive and larger than unity, with a median εP(P, R) of 1.28–2.04. The results strongly reaffirm the role of rainfall as the main driver of runoff changes due to its faster response relative to change in precipitation at the Indochina Region. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important atmospheric variables to assess, particularly in the context of climate change. This study evaluates future changes in precipitation over the Iberian Peninsula (IP) under the RCP8.5 scenario. Changes are assessed for two future climate periods namely (2046–2065) and (2081–2100), relative to a recent reference climate (1986–2005). Here we introduce the concept of precipitation episodes (PEs) and estimate their statistical properties for the present climate and, their changes for future climate scenarios. PEs are defined by considering a full range of durations as well as intensities. This constitutes a novel approach to estimate changes with relevance, for example, for water resources applications. The climate simulations are performed with the Weather Research and Forecast (WRF) model. These are compared with an ensemble of other similar simulations from the Coordinated Downscaling Experiment initiative. This was done to evaluate the performance of the WRF model and also to estimate uncertainty of the derived future projections. Since models may present systematic errors, results from all simulations were previously bias corrected relative to observations using the same quantile mapping method. Under climate change, a great part of the region is expected to experience reduced annual precipitation of approximately 20–40% and reaching 80% in summer by the end of the XXI century. For the PEs, a large reduction in the average number of days and duration of all types of PEs is expected across all seasons and regions. The average intensity of episodes is projected to increase in winter and spring and decrease in summer. These results imply that climate change will likely influence precipitation and precipitation extremes in the 21st century, mostly in southern areas. These, together with projected warming may amplify desertification already taking place in the southern regions of the IP and cause stresses to water resources. [ABSTRACT FROM AUTHOR]

This study examines the 1950–2017 temporal changes in climate extremes in Israel, which is located in the East Mediterranean (EM), a region which suffers from a scarcity of long and reliable datasets. It is well known that most long‐term records are affected by artificial shifts most commonly caused by station relocation, instrumental modification and local environmental changes. Therefore, for the first time, a thorough homogenization (detection and correction) routine was developed and implemented in the long‐term records. Consequently, a new daily adjusted dataset has been generated, including 34 temperature stations and 60 precipitation stations. Based on this comprehensive dataset, 38 extreme indices recommended by the Expert Team on Climate Change Detection and the Expert Team on Sector‐specific Climate Indices have been calculated. These indices will help various sectors to plan properly mitigation actions and adaptation for climate change, in addition to facilitating future studies for the EM. The results showed highly significant changes in temperature extremes associated with warming, especially for those indices derived from the daily minimum temperature (TN, 1950–2017), whereas the maximum temperature (TX) exhibited a similar increasing magnitude of the TN (~0.55°C/decade) in the last 30 years. The warming trends, which are non‐monotonic, seem to have been particularly strong since the early 1990s. The coastal area is characterized by higher heat stress during the nighttime, while mountains exhibit a strong tendency towards increasing temperatures during the noon hours. A reduction in the total precipitation amount and in the number of wet days with a tendency towards more intense wet days was found. Although all the regional trends of the precipitation indices were not statistically significant (p ≤.05), they showed a fine spatial coherence. [ABSTRACT FROM AUTHOR]

The ability of the seven CMIP5 models to simulate extreme precipitation events over Iran was evaluated using the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN‐CDR) data set. The criterion used to select the CMIP5 models was the availability of historical daily precipitation data (PERSIANN‐CDR) for the retrospective period 1983–2005, as well as future projections for the three representative concentration pathways emission scenarios (RCP2.6, RCP4.5, and RCP8.5) and spatial resolution higher than 2 × 2°. This is the first study to focus on extreme precipitation climate model simulations over Iran that includes high topography and different climates. The results show that CCSM4 has the highest correlation coefficients (CC = 0.85) and lowest root‐mean‐square error (RMSE = 73.6 mm) compared to PERSIANN‐CDR for the mean annual precipitation. However, HadGEM2‐ES shows the best (highest CCs between 0.67–0.79 and almost the lowest root‐mean‐square errors [RMSEs] compared to PERSIANN‐CDR) performance for intensity indices; MIROC5 ranked seventh (least CCs and almost the highest RMSEs) among the selected models. The results show that BCC‐CSM1‐1‐M captures maximum consecutive dry days (CDD) better than the other models. The probability matching method (PMM) is used to bias‐correct daily precipitation events from CMIP5 models with respect to the PERSIANN‐CDR estimations. All the model performances designed to capture the mean annual precipitation, as well as extreme intensity indices, improved after correction. The ensemble, constructed from the bias‐corrected model simulations using multiple linear regression (MLR), has the best performance for simulating the mean annual precipitation and extreme indices (CCs between 0.82 for consecutive wet days [CWD] and 0.93 for the mean annual precipitation) compared to the PERSIANN‐CDR estimations. Among the seven selected models, CCSM4 has the highest ranking (CCs between 0.70 for CWD to 0.91 for mean annual precipitation) after bias correction. [ABSTRACT FROM AUTHOR]

Climate change and urbanization collectively influence precipitation changes. However, their separate potential contributions to precipitation changes are not well understood due to their complex interactions. Hence, a "trajectory"‐based method was used to separate their potential contributions across national, regional and local scales in China. Precipitation changes in non‐urban regions can be regarded as representing the influence of climate change and can serve as a reference for isolating precipitation changes due to urbanization in urban areas. Our results revealed that climate change was the dominant factor for precipitation trends, while urbanization exhibited a relatively weak influence, especially for extreme precipitation at the national scale. At the regional scale, the impacts of urbanization on precipitation became more significant. About 20.2 and −30.6% of positive and negative trends in total precipitation originated from urbanization. At the local scale, the potential contribution of urbanization was strongly correlated with local environmental characteristics. Although there were differences in the potential contributions of climate change and urbanization at national, regional and local scales, climate change was the dominant factor for precipitation trends and urbanization acted as a regulator to drying or wetting due to precipitation under climate change. In general, urbanization causes a greater impact on total precipitation than on precipitation extremes. Due to this, attribution approach is static and broad‐based and does not render the level of confidence that is needed for scale‐aware attribution, future studies are needed to understand the physical mechanisms of impacts of local environment changes on precipitation trends at different geographical locations over China. [ABSTRACT FROM AUTHOR]

Projections of climate change impacts over Europe are derived using a new quantile–quantile adjustment method. E‐OBS high‐resolution gridded data sets of daily observed precipitation and 2‐m surface minimum and maximum temperatures have been used as the current climate baseline. For projections, the same meteorological variables have been obtained from a set of regional climate models (RCMs) integrated in the EURO‐CORDEX project, and by considering the RCP4.5 and RCP8.5 future emissions scenarios. To enhance the reliability of RCM data at local scale, new developments of a previous quantile–quantile adjustment have been applied to the simulated regional scenarios. This method focuses not only on the bulk spectrum of the cumulative distribution functions but also on its tails. Results show an overall improvement in reproducing the present climate baseline when using calibrated series instead of raw RCM outputs. Next, we have used these locally adjusted series to quantify the climate change signal through a number of annual and seasonal indicators. A significant increase of the minimum and maximum temperatures in all seasons is projected over Europe, being more marked in the Mediterranean for summer and autumn. Prospects on future seasonal and annual changes in precipitation are more diverse, showing an overall decrease in southern Europe and the Mediterranean, while precipitation is expected to increase towards the north of the continent. With these sources of information at hand, including and accounting for the identification of the most vulnerable geographical areas, policy makers and stakeholders can respond more effectively to the future challenges imposed by climate change. [ABSTRACT FROM AUTHOR]

This study examines potential benefits of performance‐based multi‐model ensembles (MMEs) in projecting the impacts of climate change on extreme precipitation indices over East Asia (EA) using the data from 19 GCMs in the coupled model intercomparison project 5 (CMIP5). The Taylor skill score is adopted as the measure of the model skills in simulating the spatial and interannual variability of the selected extreme precipitation indices over four EA regions. The overall rank based on the total skill score (TSC) is used to construct two skill‐based MMEs, MME of high‐skill, MMH (MME of low‐skill, MML) that include the top (bottom) seven models, in addition to the simple ensemble of all 19 GCMs (ENS). Inter‐GCM consistency is measured using the signal‐to‐noise ratio (SNR). In the present‐day period, MMH yields higher skill scores than MML and ENS for almost all extreme precipitation indices as well as regions. Regional variations in biases, inter‐model consistency, and TSC are large. The inter‐model consistency is highest for Northern China and Manchuria and is lowest for Southern China. The most notable differences in the key properties of climate change signals from the three MMEs among the three ensembles are that the climate change signals from MMH and ENS exceed the 90% significance level in much larger areas than those from MML. However, the differences in the climate change signals between MMH and MML are generally below the 90% significance level. The SNR of the projected climate change signals shows that MMH yields more consistent climate change signals than ENS/MML. Both the SNR differences and the area in which statistical significance exceed the 90% level suggest that constructing climate change signals from a group of higher‐skill models may yield more reliable projections than constructing MMEs from the entire models or a group of lower‐skilled models. The domains used in the study (SOUTH: 110°–140°E, 20°–30°N, MIDDLE: 110°–140°E, 30°–40°N), NORTH: 110°–140°E, 40°–50°N, EA: 110°–140°E, 20°–50°N) [ABSTRACT FROM AUTHOR]

Spatial and temporal patterns of trends in annual and seasonal precipitation over Germany during 1951–2013 were analysed using the ensemble empirical mode decomposition (EEMD) method. Three widely used and recognized high‐resolution observation‐based gridded precipitation products, the Climatic Research Unit (CRU) time‐series data, Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis data and the EU‐FP6 project ENSEMBLES derived data set (EOBS), were used and compared. Comparison among different products showed that the CRU used a considerably lower number of gauge stations (maximum 36) in the construction of gridded product over Germany and should be used with great caution for other applications, and overall CRU presented less precipitation and smaller inter‐annual variability; GPCC and EOBS agreed well in the spatial and temporal distribution of seasonal and annual precipitation, as well as the identified trends. The spatiotemporal analysis of trends showed that trends in precipitation during 1951–2013 were, in most cases, highly nonlinear and varying over time. In particular, the spring, summer and autumn precipitation showed large variations in trends. Therefore, the assumption of linear trends on which previous studies were based is invalid. Annual precipitation showed increasing trends in over 66% of Germany (particularly northern and eastern Germany), and the magnitude of increasing trends were generally enhanced over time, e.g. GPCC showed that the average magnitude of increasing trends rose from 6.3% for the 1951–1980 period to 12% for the 1951–2013 period. There were considerable spatial and temporal variabilities in trends in seasonal precipitation totals over Germany. This study is among one of the first studies applying the EEMD method to a comprehensive analysis of time‐varying trends in precipitation over Germany, which is expected to improve our understanding of the complex and nonlinear regional climate system. Spatial and temporal patterns of the EEMD‐derived trend of the four seasonal precipitation totals (DJF, MAM, JJA, SON; the trend is presented as the percentage of the climatology mean during the whole period 1951–2013 for easy interpretation and comparison) from EOBS for five different periods, with ending years of 1980, 1990, 2000, 2010 and 2013. [ABSTRACT FROM AUTHOR]

The arid and semi‐arid areas in Central Asia have scarce water resources and fragile ecosystems that are especially sensitive and vulnerable to climate change. Reliable information regarding future projections of change in climate is crucial for long‐term planning of water resources management and structural adjustment of agriculture in this region. However, the low‐density meteorological observation network brings great challenges for investigating the effects of climate variations. In this study, variations of precipitation and temperature in Central Asia were examined by a combination of gridded climate dataset of the Climate Research Unit and the latest general circulation models (GCMs) under a representative concentration pathway 4.5. Three downscaling methods, Delta, Advanced Delta, and Bayesian model averaging (BMA) methods, translate the coarse GCMs to local climatic variations for the period 2021–2060 relative to 1965–2004. Major results suggested that the Advanced Delta and BMA methods outperformed the Delta method in precipitation downscaling. Projected precipitation exhibited a general increasing trend at a rate of 4.63 mm/decade for entire Central Asia with strong spatiotemporal heterogeneity. While a declining trend was observed in the southwestern and central parts of Central Asia in summer. The projected temperature was revealed an obvious ascending at 0.37°C/decade, while the warming rate accelerated in higher latitude and mountainous areas. [Correction added on 03 December 2018, after first online publication: The preceding statement has been corrected in this version.] The surface land coverage had significant effects on the variations of precipitation and temperature, respectively. The driven factors of local climate were suggested by analysing the relationships between climate variations and large‐scale atmospheric circulation fields anomalies. The findings of this study aims to provide useful information to improve our understanding for future climate change and benefit local decision makers. The performance of three downscaling methods in terms of precipitation and temperature from general circulation model (GCM) outputs were compared in this study. The relationships between climatic variations and different land cover types were detected based on Land‐Use and Land‐Cover Change (LUCC) and NDVI datasets. The possible drivers of local climatic variations were also suggested by analysing the relationships between local climatic variations and large‐scale atmospheric circulation fields. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the long‐term trends in the distribution of global monsoon precipitation over recent decades using the Global Precipitation Climatology Project (GPCP) data set. In addition, the potential4 mechanisms are examined by applying the atmospheric moisture budget decomposition method to the outputs from the ERA‐Interim medium‐range weather forecasting reanalysis produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF). Changes in global monsoon precipitation are primarily thermodynamically caused by changes in specific humidity and dynamically caused by changes in circulation, as well as by changes in moisture transport that are driven by transient eddies and local evaporation. The results show that the increasing trends in global summer monsoon precipitation from 1979 to 2016 were dominated by contributions from the southern Asian, South African, and North American monsoon regions and were driven by the response of atmospheric circulation to the enhanced east–west thermal contrast in the tropical Pacific. We find that changes in the sea surface temperature (SST) patterns promote increased low‐level vertical velocities in monsoon regions, leading to moisture convergence through divergent circulation anomalies when combined with the climatological humidity field. Hence, the processes result in increasing trends in monsoon precipitation. In addition, evaporation makes increasing contributions to monsoon precipitation throughout the world, except in the South American monsoon region. Moreover, subtropical regions in the East Pacific (the Atlantic) may be major sources of water vapour that have supported increases in precipitation over the southern Asian and North American monsoon regions (the South African monsoon region) in recent decades. Linear trends in (a) precipitation, (b) the moisture convergence term, (c) evaporation, and (d) residual term (precipitation minus the divergence of vertically integrated moisture transport term and evaporation) in local summer (May–September in the Northern Hemisphere and November–March in the Southern Hemisphere) from 1979 to 2016 (unit: mm day−1 year−1). The black curves depict the monsoon domains. Stippled regions indicate statistical significance at the 95% level or higher, as determined using the Mann–Kendall trend test. [ABSTRACT FROM AUTHOR]

This article proposes to use the Köppen classification to evidence climate change in Brazil. To do so, the average monthly temperature and precipitation data were computed for a set of 208 stations, representative of the climatic diversity of Brazil for the period from 1964 to 2015. The originality of our approach is not only to look at the shift between the averages of two references periods but also to observe how each year can be classified using Köppen's classification; thus, we defined "annual climate types" (ACT) and established the frequency of ACTs for each station and each period of 26 years: 1964–1989 and 1990–2015. The statistical and cartographic treatment of this information makes it possible to define the limits of the climates of Brazil and look at its evolution showing the diversity of regional situations in Brazil. The results show that 35 stations (17%) changed from the average Köppen's type, with significant regression of wet tropical types (Af and Am) and temperate types (C). On the other hand, the tropical (Aw), arid and semi‐arid (B) types progress between the two periods showing significant modifications of the climatic limits in Brazil: extension of aridity in the northeast of Brazil and increasing extinction of tropical types upon temperate climates in the south of the country and upon rainy types in the south of the Amazon. These results are consistent with other studies and illustrate the potential for climate change and variability monitoring using a simple classification method. The Köppen's classification is very often used in climatology. However, almost all the works use it on average data over periods of 20 or 30 years, thus erasing the inter‐annual variability of climate. The originality of our study is to use this classification on an annual scale to calculate frequencies of "types of annual climate" for two periods and to compare them. The study focuses on Brazil, which presents two additional interests: such a frequency approach has never been realized there to study climate change; the diversity of climates and changes observed in this country makes it possible to test the robustness of the method on different climates (from equatorial to semi‐arid and temperate types). In the figure, frequency of TCA per station for the period 1990–2015. [ABSTRACT FROM AUTHOR]

In the present study, four different bias correction methods are assessed in improving the precipitation output from a set of two regional climate simulations for 1971–2005 period. The simulations come from Coordinated Regional Climate Downscaling Experiment over South Asia (CORDEX‐SA). The bias correction methods applied here are linear scaling (Scaling), empirical quantile mapping (EQM), power transformation (PTR) and local intensity scaling (LOCI). All the bias correction techniques show skill in improving the model results, but there are clear differences in the degrees of success depending upon the characteristic of precipitation studies—like seasonal mean, seasonal extremes or inter‐annual variability. Most of the methods show equivalently high ability in correcting the seasonal mean precipitation values; however, there also exist residual biases which vary spatially over India. The largest reduction in bias was observed for the frequency of dry days and precipitation on very wet days, which are significantly altered after adjustment by EQM and PTR. It is found that distribution‐based EQM method is more skilful followed by PTR and LOCI in most of the cases including correction of frequency‐based indices (e.g., dry days number and inter‐annual variability). (a) Climatology of JJAS mean precipitation (mm/day) (1971–2005) from (A) IMD, (B) uncorrected values from RCA4, (C–F) same as (B) but bias corrected by different methods, respectively, scaling, EQM, PTR and LOCI. (b) Same as (a) but their biases against IMD. [ABSTRACT FROM AUTHOR]

Frequency analysis of precipitation extremes is significant for the selection of design rainfalls, which are essential inputs for the design of water infrastructure projects, especially when the climate has changed. Therefore, the objective of this study was to propose a framework for more reasonably analysing the frequency of extreme rainfalls. The proposed framework consists of a maximum likelihood estimate (MLE) method for analysing the parameter trends, a hydrological variation diagnosis system to determine abrupt change times, generalized extreme value (GEV) and generalized Pareto distribution (GPD) models for frequency analysis of precipitation extremes, and an ensemble‐methods approach for choosing the most appropriate distributions. The methodology was successfully implemented using a 52‐year time series (1963–2014) of rainfall data recorded by eight rain gauges in Chengbi River basin (south China). The results show that the rainfall series mutated in 1993 and that the entire data set could be divided into two slices (1963–1992 and 1993–2014). Climate change was found to have some impacts on the precipitation extremes: the extreme rainfall value and the parameters of GEV and GPD were variable in the context of climate change. Furthermore, the GPD distribution model outperformed the GEV distribution model. The location of Chengbi River basin and the distribution of the rainfall stations, and the representative rainfall stations are shown as red stars. [ABSTRACT FROM AUTHOR]

Drought is viewed as a naturally recurring phenomenon in many Australian agricultural systems. Identifying regional changes in frequency and severity of drought induced by climate change is required to develop regionally specific adaptation strategies. In this study, we provided a first look at the impacts of climate change on 21st century drought characteristics over the New South Wales wheat belt of southeastern Australia. These impacts were assessed from an ensemble of 28 statistical downscaled global climate models under representative concentration pathway (RCP8.5). A modified relative standardized precipitation and evapotranspiration index (rSPEI) at the seasonal scale (3 months) was used to analyse temporal and spatial changes in drought. Results indicated that there was a tendency towards more frequent and severe winter–spring droughts over the study area. Moreover, winter–spring drought prone areas were expected to expand from west to east. Until the end of the 21st century, more than half the wheat belt would be vulnerable to winter–spring drought. The combined effects of reduced precipitation and increased temperature during future winter and spring seasons were the main reasons causing these changes of drought. In addition, summer and autumn droughts would have both slight temporal and spatial changes across the study region. This study also revealed that traditionally dry areas would likely experience an increased frequency of drought compared to wetter areas when subjected to a same increase in temperature or decrease in precipitation. Furthermore, the western part of the wheat belt might be unsuitable for winter crops in the future, or at least exposed to an increased risk of variable yield and would require a gradual transformation which might include summer crops or pastures. Investments in cropping land should be focused on the east part of the wheat belt to achieve more consistent financial returns. Drought prone areas were identified in the study region using the modified drought index, rSPEI. Spring and winter drought prone areas were likely to expand remarkably. By the end of the 21st century, over half of the study area was at a high risk of suffering spring and winter drought. Summer and autumn drought prone areas were primarily in the southwest and northwest and might only experience slight changes. [ABSTRACT FROM AUTHOR]

Five extreme precipitation indicators were calculated on an annual basis for 1890 through 2013 and analysed to determine spatial patterns and temporal trends in the frequency and magnitude of observed extreme precipitation events in Kansas located in the central United States. Indicators were selected from the list of the World Meteorological Organization–Commission for Climatology (WMO–CCL) and the Research Programme on Climate Variability and Predictability (CLIVAR). The indicators included the number of days with precipitation greater than or equal to 10 mm/day (R10), maximum number of consecutive dry days (CDD; days with precipitation lower that 1 mm), maximum 5‐day precipitation total (R5D), and simple daily intensity index (SDII) which is the annual precipitation total divided by number of days with precipitation greater than or equal to 1 mm, and the fraction of annual total precipitation due to events exceeding the 95th percentile (R95T) based on the period 1961–1990. Positive trends in the results were found for a majority of stations for R10, R5D, SDII, and R95T. Consecutive dry days was the only index that had a negative trend at a majority of stations. Spatial pattern analysis indicates greater changes in frequencies and magnitudes for eastern Kansas. Results from this study highlight observed climate shifts in precipitation patterns with a tendency towards greater and more frequent extreme precipitation in eastern Kansas and a tendency towards drier conditions in western Kansas. These hydroclimatic adjustments can produce costly impacts in areas that include flood management, hydraulic structures, water availability throughout the year, agricultural production, ecosystems, and human health. Results from five extreme precipitation indicators highlight observed climate shifts in precipitation patterns with a tendency towards greater and more frequent extreme precipitation in eastern Kansas and a tendency towards drier conditions in western Kansas. The increasing trends for the wet condition indicators, particularly in the eastern Kansas, emphasize the necessity to update water management techniques and maintain water control structures adequately in a variable and changing climate. Western Kansas is faced with prolonged strings of dry weather and a lack of enough water in the growing season has resulted in water withdrawals from underground resources. [ABSTRACT FROM AUTHOR]

Multi‐member ensembles of climate models are used to study the role of internal climate variability and its potential impact on climate change impact studies. The reliability of such ensembles with respect to representing the internal climate variability is generally implicitly accepted. Using the latest version of the Climate Research Unit (CRU) data set as a baseline, this study verifies the reliability of multi‐member ensembles in estimating the internal precipitation and temperature variability at the multi‐decadal scale. To achieve this, multi‐decadal variability calculated using climate model multi‐member ensembles is first compared to that calculated using CRU data. The inter‐member variability of a single climate model is then compared to the multi‐decadal variability of CRU precipitation and temperature. Three climate models, with the number of members ranging between 5 and 40, are used to investigate whether the reliability is dependent upon the number of members of a climate model. The results show that multi‐member ensembles are capable of capturing the observed spatial pattern of multi‐decadal variability for both precipitation and temperature at the global scale. However, multi‐member ensembles perform better over regions with smaller variability. For the variables and timescale investigated, all three multi‐member ensembles show similar performances, suggesting that a five‐member ensemble may be sufficient to estimate the internal multi‐decadal climate variability for the chosen variables. Overall, this study indicates that the multi‐member ensemble of a climate model can be used to estimate the internal climate variability of annual and seasonal precipitation and temperature at the multi‐decadal and regional scales, if long historical records are not available. Climate model multi‐member ensembles are capable of capturing the observed spatial pattern of MDV for both precipitation and temperature. Climate model multi‐member ensembles perform better over regions with smaller variability. Performance of multi‐member ensembles did not depend on the number of members within each ensemble for annual and seasonal precipitation at the multi‐decadal scale. In the figure, multi‐decadal variability of mean annual precipitation estimated using CRU data and different climate model ensembles. Different colors represent the standard deviation of precipitation in millimeters. [ABSTRACT FROM AUTHOR]

In this study, we assess whether dynamical downscaling using a regional climate model (RCM) would provide added value against the driving global climate model (GCM) and whether a statistically bias‐corrected GCM could be used as an alternative to a bias‐corrected RCM for assessing the hydrological response to climate change. The target region is the Chungju Basin in Korea, which is characterized by complex geographical features. The analysis focuses on runoff simulations driven by raw outputs and bias‐corrected outputs from the HadGEM2‐AO and RegCM4, which are used to generate national standard climate change scenarios throughout the Korean Peninsula. Statistical methods, such as linear scaling and quantile mapping, improve the performance in precipitation simulations and effectively correct the systematic biases seen with the climatological means of the HadGEM2‐AO and RegCM4. Although there is no relevant difference between the bias‐corrected HadGEM2‐AO and RegCM4 in terms of long‐term averaged climatology, the bias‐corrected RegCM4 performs better in capturing the dominant mode of runoff variability and hydrological extremes (e.g., floods measured by 1‐day maximum runoff and droughts measured by the standardized runoff index). Because the magnitude of the bias correction factor tends to influence the preservation of future change signals derived from raw and bias‐corrected outputs, the HadGEM2‐AO, which requires a large bias correction factor to compensate for poor performance, produces future changes in high flows with low coherence between the raw and bias‐corrected projections. A distributed hydrological model (VIC) at the basin level is performed with different meteorological input to assess whether RCM provides added value against driving GCM and whether statistical bias correction is sufficient to improve GCM projections. GCM produces future changes in yearly maximum 1‐day runoff with low coherence between the raw and bias‐corrected projections due to a large magnitude of the bias correction factor, whereas RCM with a relatively smaller bias tends to preserve future change signals in high flows. [ABSTRACT FROM AUTHOR]

Understanding the changes in aridity is crucial because of the substantial impacts it can have on human society. However, realistic aridity changes and their underlying climatic mechanisms demonstrate large uncertainties in China. Here we adopted the aridity index (AI) to assess the aridity changes in China, and we quantified the relative contributions of various climatic drivers (the precipitation, net radiation, wind speed, relative humidity, and air temperature) to AI changes based on the station data during 1961–2016. Our results showed that the arid/semi‐arid northwest China and Tibet Plateau experienced wetting trends, while western sub‐humid north and northeast China and western humid south China exhibited drying trends during 1961–2016. The dominant climatic drivers involved in the changing AI trends changed from arid to humid regions. In arid northwest China, increasing precipitation and decreasing wind speeds contributed the most to the wetting trends (0.024 and 0.017 in 56 years, respectively). In the semi‐arid Tibet Plateau, increasing precipitation dominated the wetting trends (0.045 in 56 years). However, in sub‐humid/humid north and northeast China and south China, decreasing wind speed and net radiation dominated the annual AI changes (0.061 and 0.092 in 56 years, respectively). In addition, AI of northwest China and the Tibet Plateau exhibited a significant abrupt change in 1987 and exhibited wetter states afterwards, which could be caused by the abrupt increase in precipitation in 1987. This study could improve current understanding of the aridity changes in China and help adapt its potential impacts. This study investigated the aridity changes and their underlying climatic mechanisms in China using station data during 1961–2016. Results showed that the "arid regions getting drier; humid regions getting wetter" is not applicable to China. The dominant climatic driver for aridity changes varies from arid to humid regions. Precipitation dominates the wetting trends in arid/semi‐arid northwest China and the Tibet Plateau, whereas wind speed and net radiation dominate aridity changes in sub‐humid/humid north and northeast China and south China. [ABSTRACT FROM AUTHOR]

Two versions of the "reliability ensemble averaging" (REA and REA Xu) method and an unweighted mean were used to generate multi‐model ensembles of 14 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for a baseline (1971–2000) period and future scenarios (RCP4.5 and RCP8.5) for the 21st century. To test the consistency of the REA ensembles at different scales, one ensemble was area‐averaged at regional scale and two were obtained at 50‐km gridpoints. Climatic metrics of temperature and precipitation during the baseline period served to evaluate the performance of the GCMs and the ensembles in the North American monsoon (NAM) and the mid‐summer drought (MSD) regions. The metrics of the three REA ensembles are very similar among them in each region and show a better performance than the unweighted multi‐model ensemble (U‐MME). The majority of the GCMs overestimate winter precipitation in the NAM and fail to retract the end of the monsoon in autumn; REA ensembles reduce this overestimation. All ensembles capture well the MSD's double peak of rainfall, but underestimate summer precipitation. According to all ensembles, temperature increases of 1.5–2 °C in the two regions may be reached between 2035 and 2055 relative to the baseline, and by 2070–2099 temperature (precipitation) in Mexico may increase (decrease) between 2° C (5%) and 5.8 °C (10%) in the RCP4.5 and RCP8.5 scenarios, respectively. Annual changes of precipitation show a north (positive) and south of 35°N (negative) pattern. The largest impacts are expected during summer with a possible decrease of ~13% (up to −1.5 mm/day), especially in southern Mexico, Central America, and the Caribbean, while autumn precipitation may slightly increase. Future projections from the REA Xu (by gridpoint) ensemble show spatial patterns similar to the U‐MME, but with more regional detail, which could be an added value for regional climate impact studies. Two versions of the "reliability ensemble averaging" (REA and REA Xu) method and the unweighted multi‐model ensemble (U‐MME) were used to generate CMIP5 ensembles for a baseline period (1971–2000) and 21st century scenarios (RCP4.5 and RCP8.5) for Mexico, the North American monsoon, and the mid‐summer drought region. REA ensembles show better performance than U‐MME. Future temperature and precipitation changes from REA Xu show more regional detail than the U‐MME, which could be an added value for regional climate impact studies. Maps of seasonal (DJF and JJA) precipitation change in 2070–2099 with respect to 1971–2000 in RCP8.5 scenarios according to the U‐MME and the REA Xu ensemble. [ABSTRACT FROM AUTHOR]

Future changes in the annual cycle of the precipitation in central Asia (CA) were estimated based on the historical and representative concentration pathway 8.5 (RCP8.5) experiments from 25 models of the Coupled Model Intercomparison Project phase 5. Compared with the Global Precipitation Climatology Project observations, the historical (1979–1999) experiments showed that most models can capture the migration of rainfall centres, but remarkable discrepancies exist in the location and intensity of rainfall centres between simulations and observations. Considering the skill scores of precipitation and pattern correlations of circulations, which are closely related to the precipitation for each month, for the 25 models, the four best models (e.g., CanESM2, CMCC‐CMS, MIROC5 and MPI‐ESM‐LR) with relatively good performance were selected. The four models' ensemble mean indicated that the migration and location of the precipitation centres were better reproduced, except the intensity of the centres was overestimated, compared with the result that only took the precipitation into consideration. Based on the four best models' ensemble mean under RCP8.5 scenarios, precipitation was projected to increase dramatically over most of the CA region in the boreal cold seasons (November, December, January, February, March and April) and May, with the maximum in December at the end of 21st century (2079–2099), and several positive centres were located in the Pamirs Plateau, the Tianshan Mountains and the northern Himalayas. The precipitation changes were weak in the boreal warm seasons (June, July, August, September and October), with a dry centre located in the Pamirs Plateau and a wet centre in the northern Himalayas. These projected changes can be directly attributed to the overlay of anomalous moisture convergence and evaporation, and other possible mechanisms of dynamic and thermodynamic factors need to be further elucidated. Annual cycle of precipitation (unit: mm/day) averaged by (a) latitudes (35–55°N) and (b) regions of 35–55°N and 50–95°E during 1979–1999 from the GPCP observations [ABSTRACT FROM AUTHOR]