Daily Precipitation Statistics Research Articles

A Comparison of CMIP3 Simulations of Precipitation over North America with Observations: Daily Statistics and Circulation Features Accompanying Extreme Events

Abstract Climate model simulations of daily precipitation statistics from the third phase of the Coupled Model Intercomparison Project (CMIP3) were evaluated against precipitation observations from North America over the period 1979–99. The evaluation revealed that the models underestimate the intensity of heavy and extreme precipitation along the Pacific coast, southeastern United States, and southern Mexico, and these biases are robust among the models. The models also overestimate the intensity of light precipitation events over much of North America, resulting in fairly realistic mean precipitation in many places. In contrast, heavy precipitation is simulated realistically over northern and eastern Canada, as is the seasonal cycle of heavy precipitation over a majority of North America. An evaluation of the simulated atmospheric dynamics and thermodynamics associated with extreme precipitation events was also conducted using the North American Regional Reanalysis (NARR). The models were found to capture the large-scale physical mechanisms that generate extreme precipitation realistically, although they tend to overestimate the strength of the associated atmospheric circulation features. This suggests that climate model deficiencies such as insufficient spatial resolution, inadequate representation of convective precipitation, and overly smoothed topography may be more important for biases in simulated heavy precipitation than errors in the large-scale circulation during extreme events.

Effect of Global Warming on Intensity and Frequency Curves of Precipitation, Case Study of Northwestern Iran

Predicting natural phenomena like flood and drought and consequently presenting an appropriate solution for fighting such natural hazards in northwestern Iran is considered in this study through clustering the precipitation regime. To compare the reference period (past) with the simulated data, the statistics of daily precipitation in six stations of Ardebil, Ghazvin, Hamedan, Kermanshah, Sanandaj, and Tabriz, have been provided for a 30-year period (1961–1990) and compared with the simulated data of 2021–2050. The simulated data was generated by general atmosphere circulation model HADCM3, A1scenario and was downscaled using the LARS-WG model. The method for comparing precipitations was done based on clustering in the form of 5 clusters for all the stations and study periods. One of the results of this research is the greater concentration of precipitation for the cold periods of the year (winter and fall) and the increase of annual precipitation by the amount of 20.62 mm for future period. Furthermore, the normality of two coordinates of precipitation and cluster frequency percentage was evaluated. The outputs of this section demonstrate that the precipitations of cluster 3 which have the features of light rain with average intensity, fall on this normal line for most of the stations and study periods. On the other hand, precipitations of cluster 1 indicating very heavy and intense precipitations, have the most distance to the normal line in most cases. Therefore, the precipitations of the third cluster need optimal exploitation management while those of the first cluster need required risk and crisis management.

Uncertainties in daily rainfall over Africa: assessment of gridded observation products and evaluation of a regional climate model simulation

ABSTRACTWe intercompare three gridded observed daily rainfall datasets over Africa (FEWS (Famine Early Warning System), GPCP (Global Precipitation Climatology Project) and TRMM (Tropical Rainfall Measuring Mission)) in order to assess uncertainties in observation products towards the evaluation of the performance of a Regional Climate Model (RegCM3) in simulating daily precipitation characteristics over a domain encompassing the whole African continent. We find that different observation products exhibit substantial systematic differences in mean rainfall, but especially in higher order daily precipitation statistics, such as frequency of wet days, precipitation intensity and extremes as well as maximum length of wet and dry spells. For example, FEWS shows mostly higher frequency and lower intensity events than TRMM and GPCP. Thus, the different datasets provide quite different representations of daily precipitation behavior. As a result, although RegCM3 captures pretty well the monsoon rainband evolution and exhibits a representation of daily precipitation statistics within the range of the observations, it performs differently with respect to the various products. For instance, it simulates more intense but less frequent events over East and Southern Africa than in FEWS and vice versa compared to TRMM. We thus highlight the uncertainty in observations as a key factor preventing a rigorous and unambiguous evaluation of climate models over Africa. Improving the quality and consistency of observation products is thus paramount for a better understanding of the response of African climate to global warming. Copyright © 2012 Royal Meteorological Society

Estimated effects of climate change on flood vulnerability of U.S. bridges

We assessed the potential impacts of increased river flooding from climate change on bridges in the continental United States. Daily precipitation statistics from four climate models and three greenhouse gas (GHG) emissions scenarios (A2, A1B, and B1) were used to capture a range of potential changes in climate. Using changes in maximum daily precipitation, we estimated changes to the peak flow rates for the 100-year return period for 2,097 watersheds. These estimates were then combined with information from the National Bridge Inventory database to estimate changes to bridge scour vulnerability. The results indicate that there may be significant potential risks to bridges in the United States from increased precipitation intensities. Approximately 129,000 bridges were found to be currently deficient. Tens of thousands to more than 100,000 bridges could be vulnerable to increased river flows. Results by region vary considerably. In general, more bridges in eastern areas are vulnerable than those in western areas. The highest GHG emissions scenarios result in the largest number of bridges being at risk. The costs of adapting vulnerable bridges to avoid increased damage associated with climate change vary from approximately $140 to $250 billion through the 21st century. If these costs were spread out evenly over the century, the annual costs would be several billion dollars. The costs of protecting the bridges against climate change risks could be reduced by approximately 30% if existing deficient bridges are improved with riprap.

Evaluation of the weather generator CLIGEN with daily precipitation characteristics in Korea

The ability of the CLImate GENerator (CLIGEN) weather generator to reproduce daily precipitation characteristics for Korea was assessed on the basis of 55-year long historical daily precipitation records from eight weather stations (Seoul, Incheon, Daegu, Ulsan, Gwangju, Busan, Kangneung, and Jeonju) representing different parts of the Korean peninsula. The basic statistics of daily precipitation (mean, standard deviation, skewness of daily precipitation, number of rainy days, and the lengths of wet/dry period), probability distribution characteristics of daily precipitation (percentiles and maximum value), and the spatial covariance statistic generated by CLIGEN were compared with those derived from the observed weather series. Significance tests were conducted on the difference between the historical and generated statistics with the 1% significance level. The results show that CLIGEN simulates most of the daily precipitation characteristics satisfactorily with a tendency to slightly underestimate the mean and variability of daily precipitation. Especially, the number of rainy days is perfectly reproduced with mean relative error of 0.4% across all the stations. It is also found that the spatial covariance statistic from eight different stations is well reproduced by CLIGEN with respect to the leading EOF mode of summer season daily precipitation.

Daily Precipitation Statistics for South America: An Intercomparison between NCEP Reanalyses and Observations

Abstract In this study, the authors document the extent to which the precipitation statistics of the new CFS reanalysis (CFSR) represent an improvement over the earlier reanalyses: the NCEP–NCAR reanalysis (R1) and the NCEP–DOE Second Atmospheric Model Intercomparison Project (AMIP-II) reanalysis (R2). An intercomparison between the CFSR, R1, R2, and observations over South America was made for the period 1979–2006. The CFSR shows notable improvements in the large-scale precipitation patterns compared with the previous reanalyses (R1 and R2). In spite of these improvements, the CFSR has substantial biases in intensity and frequency of occurrence of rainfall events. Over west-central Brazil, the core region of the South American monsoon system (SAMS), the CFSR displays a dry bias during the onset phase of the SAMS wet season and a wet bias during the peak and decay phases of the SAMS wet season. The CFSR also displays a dry bias along the South American coast near the mouth of the Amazon and along the east coast of northeastern Brazil. A wet bias exists in all seasons over southeast Brazil and over the Andes Mountains.

Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 44:135-150 (2010) - DOI: https://doi.org/10.3354/cr00932 Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models Erik Kjellström1,*, Fredrik Boberg2, Manuel Castro3, Jens Hesselbjerg Christensen2, Grigory Nikulin1, Enrique Sánchez3 1Rossby Centre, Swedish Meteorological and Hydrological Institute, 60176 Norrköping, Sweden 2Danish Climate Centre, Danish Meteorological Institute, Lyngbyvej 100, 2100 Copenhagen Ø, Denmark 3Facultad de Ciencias del Medio Ambiente, Universidad de Castilla-La Mancha (UCLM), Toledo, Spain *Email: erik.kjellström@smhi.se ABSTRACT: We evaluated daily and monthly statistics of maximum and minimum temperatures and precipitation in an ensemble of 16 regional climate models (RCMs) forced by boundary conditions from reanalysis data for 1961–1990. A high-resolution gridded observational data set for land areas in Europe was used. Skill scores were calculated based on the match of simulated and observed empirical probability density functions. The evaluation for different variables, seasons and regions showed that some models were better/worse than others in an overall sense. It also showed that no model that was best/worst in all variables, seasons or regions. Biases in daily precipitation were most pronounced in the wettest part of the probability distribution where the RCMs tended to overestimate precipitation compared to observations. We also applied the skill scores as weights used to calculate weighted ensemble means of the variables. We found that weighted ensemble means were slightly better in comparison to observations than corresponding unweighted ensemble means for most seasons, regions and variables. A number of sensitivity tests showed that the weights were highly sensitive to the choice of skill score metric and data sets involved in the comparison. KEY WORDS: Regional climate models · Probability distributions · Skill score metrics · Weighted ensemble · Europe Full text in pdf format PreviousNextCite this article as: Kjellström E, Boberg F, Castro M, Christensen HJ, Nikulin G, Sánchez E (2010) Daily and monthly temperature and precipitation statistics as performance indicators for regional climate models. Clim Res 44:135-150. https://doi.org/10.3354/cr00932Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 44, No. 2-3. Online publication date: December 09, 2010 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2010 Inter-Research.

Intercomparison of Daily Precipitation Statistics over the United States in Observations and in NCEP Reanalysis Products

Abstract A comparison of the statistics of daily precipitation over the conterminous United States is carried out using gridded station data and three generations of reanalysis products in use at the National Centers for Environmental Prediction (NCEP). The reanalysis products are the NCEP–NCAR reanalysis (Kalnay et al.), the NCEP–Department of Energy (DOE) reanalysis (Kanamitsu et al.), and the NCEP Climate Forecast System (CFS) reanalysis (Saha et al.). Several simple measures are used to characterize relationships between the observations and the reanalysis products, including bias, precipitation probability, variance, and correlation. Seasonality is accounted for by examining these measures for four nonoverlapping seasons, using daily data in each case. Relationships between daily precipitation and El Niño–Southern Oscillation (ENSO) phase are also considered. It is shown that the CFS reanalysis represents a clear improvement over the earlier reanalysis products, though significant biases remain. Comparisons of the error patterns in the reanalysis products provide a suitable basis for confident conversion of the Climate Prediction Center (CPC) operational monitoring and prediction products to the new generation of analyses based on CFS.

The impact of climate change on daily precipitation statistics in Jordan and Israel

AbstractA regional climate model is used to investigate changes in Israel and Jordan precipitation at the end of the 21st century on daily to monthly timescales. The model predicts that this region will get significantly drier at the peak of the rainy season, reflecting a reduction in both the frequency and duration of rainy events. These changes may be associated with a reduction in the strength of the Mediterranean storm track. Copyright © 2009 Royal Meteorological Society

Comparison of Daily Precipitation Statistics for the United States in Observations and in the NCEP Climate Forecast System

Abstract An intercomparison of the statistics of daily precipitation within seasonal climate over the conterminous United States is carried out using gridded station data and output from the NCEP Climate Forecast System (CFS). Differences in the occurrence of daily precipitation between the observations and a set of CFS reforecasts are examined as a function of forecast lead time for 1982–2005. Difference patterns show considerable evolution depending on season and lead time, with positive biases in CFS at most locations and leads except along the southern tier of states during the spring and summer months. An examination of differences in daily precipitation statistics by ENSO phase and in the frequencies of wet and dry spells is also conducted using a longer period of gridded daily station data (1948–2006) and a pair of 100-yr CFS coupled simulations. These comparisons expose additional details of the regional and seasonal dependence of the bias in the CFS simulations and reforecasts over the conterminous United States. The analysis motivates additional synoptic studies aimed at improving the linkage between daily precipitation and related circulation features in CFS. Prospects for using this information to develop more reliable ensemble-based probabilistic forecasts in real time at leads of 2–4 weeks (e.g., risks of heavy rain events) are also considered.

Sensitivity of summer precipitation simulated by the CLM with respect to initial and boundary conditions

The knowledge of the uncertainty resulting from the sensitivity of a regional climate model to physical parameterisation schemes, horizontal resolution, domain size and location, as well as to initial and boundary conditions is crucial for the interpretation of model results, e.g. for dynamical downscaling of global climate predictions. In this study, we assess some of these aspects by investigating daily precipitation statistics simulated by the regional climate model CLM (Climate version of the Local Model). Different sensitivity experiments related to initialisation date, domain size and location and to the lateral boundary forcing have been performed for the summer of 1974. The evaluation domain is Germany, located approximately in the centre of the model domain. Comparisons to the control run show that the spatially averaged precipitation statistics can be significantly affected by this modification, not only in specific regions, but also in entire Germany. The results also indicate that the strength of the lateral boundary forcing has a crucial influence on the simulated characteristics.

Relationships between Climate Variability and Fluctuations in Daily Precipitation over the United States

Abstract Fluctuations in the frequency of daily precipitation occurrence and in the intensity of daily precipitation over the United States during the period 1948–2004 are identified and linked to leading sources of interannual and interdecadal climate variability. The El Niño–Southern Oscillation (ENSO) phenomena are implicated in interannual fluctuations while the Pacific decadal oscillation (PDO) and the Arctic Oscillation (AO) are linked to recent interdecadal fluctuations. For the conterminous United States as a whole there have been increases in the annual frequency of occurrence of wet days and heavy precipitation days and in the mean daily and annual total precipitation over the past several decades, though these changes have not been uniform. The possibility of significant natural forcing of these interdecadal variations in precipitation is explored. It is shown that the PDO is associated with these fluctuations over the western and southern United States, while the AO is also associated with them but to a much lesser extent over the southeastern United States. Because the interdecadal fluctuations are linked to changes in the global-scale circulation and sea surface temperatures associated with the PDO, the results imply that a significant portion of the skill of climate models in anticipating fluctuations in daily precipitation statistics over the United States will arise from an ability to forecast the temporal and spatial variability of the interdecadal shifts in tropical precipitation and in the associated teleconnection patterns into the midlatitudes.

Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps

This paper compares six statistical downscaling models (SDMs) and three regional climate models (RCMs) in their ability to downscale daily precipitation statistics in a region of complex topography. The six SDMs include regression methods, weather typing methods, a conditional weather generator, and a bias correction and spatial disaggregation approach. The comparison is carried out over the European Alps for current and future (2071–2100) climate. The evaluation of simulated precipitation for the current climate shows that the SDMs and RCMs tend to have similar biases but that they differ with respect to interannual variations. The SDMs strongly underestimate the magnitude of the year‐to‐year variations. Clear differences emerge also with respect to the year‐to‐year anomaly correlation skill: In winter, over complex terrain, the better RCMs achieve significantly higher skills than the SDMs. Over flat terrain and in summer, the differences are smaller. Scenario results using A2 emissions show that in winter mean precipitation tends to increase north of about 45°N and insignificant or opposite changes are found to the south. There is good agreement between the downscaling models for most precipitation statistics. In summer, there is still good qualitative agreement between the RCMs but large differences between the SDMs and between the SDMs and the RCMs. According to the RCMs, there is a strong trend toward drier conditions including longer periods of drought. The SDMs, on the other hand, show mostly nonsignificant or even opposite changes. Overall, the present analysis suggests that downscaling does significantly contribute to the uncertainty in regional climate scenarios, especially for the summer precipitation climate.

Trends of heavy precipitation and wet and dry spells in Switzerland during the 20th century

The low-frequency variability of heavy precipitation and drought conditions is investigated for 104 rain-gauge stations in Switzerland covering the 20th century. This constitutes an exceptionally dense data set of centennial time series. The investigation is based on a wide range of daily and multi-day precipitation statistics encompassing basic characteristics, measures of heavy precipitation and indices of typical and extreme dry and wet spells. Two different methods of trend analysis and statistical testing are applied, depending on the data nature of the statistic. Linear regression is used for statistics with a continuous value range, and logistic regression is used for statistics with a discrete value range. The trends are calculated on a seasonal basis for the years 1901–2000. A clear trend signal is found for winter and autumn, with a high number of sites with a statistically significant trend. In winter, significant increases are found for all statistics related to precipitation strength and occurrence. The centennial increase is between 10 and 30% for the high quantiles and the seasonal 1 day to 10 day extremes. In autumn, statistically significant increases are found only for the statistics related to heavy precipitation, whereas precipitation frequency and spell-length statistics show little systematic change. Although the winter trend signal is strongest in northern and western Switzerland, the autumn trend signal is more uniform. In spring and summer, the heavy precipitation and the spell-duration statistics did not show statistically significant trends. Sensitivity tests indicate that the winter and autumn trends are robust with respect to inhomogeneities in the rain-gauge time series. Copyright  2005 Royal Meteorological Society.

Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental United States

We analyze simulations of the global climate performed at a range of spatial resolutions to assess the effects of horizontal spatial resolution on the ability to simulate precipitation in the continental United States. The model investigated is the CCM3 general circulation model. We also preliminarily assess the effect of replacing cloud and convective parameterizations in a coarse-resolution (T42) model with an embedded cloud-system resolving model (CSRM). We examine both spatial patterns of seasonal-mean precipitation and daily time scale temporal variability of precipitation in the continental United States. For DJF and SON, high-resolution simulations produce spatial patterns of seasonal-mean precipitation that agree more closely with observed precipitation patterns than do results from the same model (CCM3) at coarse resolution. However, in JJA and MAM, there is little improvement in spatial patterns of seasonal-mean precipitation with increasing resolution, particularly in the southeast USA. This is because of the dominance of convective (i.e., parameterized) precipitation in these two seasons. We further find that higher-resolution simulations have more realistic daily precipitation statistics. In particular, the well-known tendency at coarse resolution to have too many days with weak precipitation and not enough intense precipitation is partially eliminated in higher-resolution simulations. However, even at the highest resolution examined here (T239), the simulated intensity of the mean and of high-percentile daily precipitation amounts is too low. This is especially true in the southeast USA, where the most extreme events occur. A new GCM, in which a cloud-resolving model (CSRM) is embedded in each grid cell and replaces convective and stratiform cloud parameterizations, solves this problem, and actually produces too much precipitation in the form of extreme events. However, in contrast to high-resolution versions of CCM3, this model produces little improvement in spatial patterns of seasonal-mean precipitation compared to models at the same resolution using traditional parameterizations.

Statistical downscaling and scenario construction of precipitation in Scania, southern Sweden

Statistical downscaling models for precipitation in Scania, southern Sweden, have been developed and applied to calculate the changes in the future Scanian precipitation climate due to projected changes in the atmospheric composition. The models are based on multiple linear regression, linking large-scale predictors at monthly time resolution to regional statistics of daily precipitation on a monthly basis. To account for spatial precipitation variability within the area, the precipitation statistics were derived for different regions in Scania. The final downscaling models, developed for different regions and seasons, use atmospheric circulation, large-scale humidity and precipitation as predictors. Among the precipitation statistics examined, only the models for estimating the mean precipitation and the frequency of wet days were skilful. Based on the Canadian Global Circulation Model 1 (CGCM1), a future scenario of these two statistics was created. The downscaled scenario shows a significant increase of the annual mean precipitation by about 10% and a slight decrease in the frequency of wet days, indicating an increase in the precipitation amounts as well as in the precipitation intensity. The main increase of precipitation amounts and intensity occur during winter, while the summer precipitation amounts decrease slightly. The seasonal changes found in precipitation are likely attributed to changes in the westerly flow of the atmospheric circulation.

Statistical downscaling of daily precipitation from observed and modelled atmospheric fields

AbstractStatistical downscaling techniques have been developed to address the spatial scale disparity between the horizontal computational grids of general circulation models (GCMs), typically 300–500 km, and point‐scale meteorological observations. This has been driven, predominantly, by the need to determine how enhanced greenhouse projections of future climate may impact at regional and local scales. As point‐scale precipitation is a common input to hydrological models, there is a need for techniques that reproduce the characteristics of multi‐site, daily gauge precipitation. This paper investigates the ability of the extended nonhomogeneous hidden Markov model (extended‐NHMM) to reproduce observed interannual and interdecadal precipitation variability when driven by observed and modelled atmospheric fields. Previous studies have shown that the extended‐NHMM can successfully reproduce the at‐site and intersite statistics of daily gauge precipitation, such as the frequency characteristics of wet days, dry‐ and wet‐spell length distributions, amount distributions, and intersite correlations in occurrence and amounts. Here, the extended‐NHMM, as fitted to 1978–92 observed ‘winter’ (May–October) daily precipitation and atmospheric data for 30 rain gauge sites in southwest Western Australia, is driven by atmospheric predictor sets extracted from National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data for 1958–98 and an atmospheric GCM hindcast run forced by observed 1955–91 sea‐surface temperatures (SSTs). Downscaling from the reanalysis‐derived predictors reproduces the 1958–98 interannual and interdecadal variability of winter precipitation. Downscaling from the SST‐forced GCM hindcast only reproduces the precipitation probabilities of the recent 1978–91 period, with poor performance for earlier periods attributed to inadequacies in the forcing SST data. Copyright © 2004 John Wiley & Sons, Ltd.

Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps

An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable‐resolution global model are considered, all with a grid spacing of 50 km. The 15‐year integrations were forced from reanalyses and observed sea surface temperature and sea ice (global model from sea surface only). The observational reference is based on 6400 rain gauge records (10–50 stations per grid box). Evaluation statistics encompass mean precipitation, wet‐day frequency, precipitation intensity, and quantiles of the frequency distribution. For mean precipitation, the models reproduce the characteristics of the annual cycle and the spatial distribution. The domain mean bias varies between −23% and +3% in winter and between −27% and −5% in summer. Larger errors are found for other statistics. In summer, all models underestimate precipitation intensity (by 16–42%) and there is a too low frequency of heavy events. This bias reflects too dry summer mean conditions in three of the models, while it is partly compensated by too many low‐intensity events in the other two models. Similar intermodel differences are found for other European subregions. Interestingly, the model errors are very similar between the two models with the same dynamical core (but different parameterizations) and they differ considerably between the two models with similar parameterizations (but different dynamics). Despite considerable biases, the models reproduce prominent mesoscale features of heavy precipitation, which is a promising result for their use in climate change downscaling over complex topography.

A stochastic rainfall model for the assessment of regional water resource systems under changed climatic condition

Abstract. A stochastic model is developed for the synthesis of daily precipitation using conditioning by weather types. Daily precipitation statistics at multiple sites within the region of Yorkshire, UK, are linked to objective Lamb weather types (LWTs) and used to split the region into three distinct precipitation sub-regions. Using a variance minimisation criterion, the 27 LWTs are clustered into three physically realistic groups or ‘states'. A semi-Markov chain model is used to synthesise long sequences of weather states, maintaining the observed persistence and transition probabilities. The Neyman-Scott Rectangular Pulses (NSRP) model is then fitted for each weather state, using a defined summer and winter period. The combined model reproduces key aspects of the historic precipitation regime at temporal resolutions down to the hourly level. Long synthetic precipitation series are useful in the sensitivity analysis of water resource systems under current and changed climatic conditions. This methodology enables investigation of the impact of variations in weather type persistence or frequency. In addition, rainfall model statistics can be altered to simulate instances of increased intensity or proportion of dry days for example, for individual weather groups. The input of such data into a water resource model, simulating potential atmospheric circulation changes, will provide a valuable tool for future planning of water resource systems. The ability of the model to operate at an hourly level also allows its use in a wider range of hydrological impact studies, e.g. variations in river flows, flood risk estimation etc. Keywords: water resources; climate change; impacts; stochastic rainfall model; Lamb weather types

Statistical downscaling of general circulation model output: A comparison of methods

A range of different statistical downscaling models was calibrated using both observed and general circulation model (GCM) generated daily precipitation time series and intercompared. The GCM used was the U.K. Meteorological Office, Hadley Centre's coupled ocean/atmosphere model (HadCM2) forced by combined CO2 and sulfate aerosol changes. Climate model results for 1980–1999 (present) and 2080–2099 (future) were used, for six regions across the United States. The downscaling methods compared were different weather generator techniques (the standard “WGEN” method, and a method based on spell‐length durations), two different methods using grid point vorticity data as an atmospheric predictor variable (B‐Circ and C‐Circ), and two variations of an artificial neural network (ANN) transfer function technique using circulation data and circulation plus temperature data as predictor variables. Comparisons of results were facilitated by using standard sets of observed and GCM‐derived predictor variables and by using a standard suite of diagnostic statistics. Significant differences in the level of skill were found among the downscaling methods. The weather generation techniques, which are able to fit a number of daily precipitation statistics exactly, yielded the smallest differences between observed and simulated daily precipitation. The ANN methods performed poorly because of a failure to simulate wet‐day occurrence statistics adequately. Changes in precipitation between the present and future scenarios produced by the statistical downscaling methods were generally smaller than those produced directly by the GCM. Changes in daily precipitation produced by the GCM between 1980–1999 and 2080–2099 were therefore judged not to be due primarily to changes in atmospheric circulation. In the light of these results and detailed model comparisons, suggestions for future research and model refinements are presented.