A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

Decreasing precipitation occurs in daily extreme precipitation intervals across China in observations and model simulations

Extreme precipitation response to increasing temperature includes not only changes of frequency and intensity, but also changes of extreme precipitation interval (EPIV) and the precipitation during the neighboring daily extreme precipitations interval (EPIP). These changes have not been fully evaluated yet in observations or climate model simulations although they are very useful to understand variations of extreme precipitation. We used daily precipitation data from 669 meteorological stations during the past five decades across China and projections of 19 general circulation models from CMIP5 under the RCP4.5 and RCP8.5 scenarios to investigate variations of EPIV and EPIP. We found the national average annual EPIV increased across China during the last five decades, while annual EPIP significantly decreased. The decreases mainly occurred in southwest China, east China, and southeast China. At national and regional scales, the average annual EPIV and EPIP showed greater decreases under the RCP8.5 scenario than those under the RCP4.5 scenario from 2006 to 2100. Annual EPIP showed a stronger correlation with extreme precipitation intensity than EPIV. The national average annual EPIP had a significant positive correlation with the Western Pacific Subtropical High Area Index. The abnormal geopotential heights over western Mongolia and the western Pacific at 500 hpa as well as the abnormal SSTs in Japan Sea and the western of Pacific in rainy seasons would result in abnormal annual EPIVs and EPIPs in China. This study may provide references for flooding prediction, water resources management, and disaster prevention and mitigation.     

Climate-change effects on extreme precipitation in central Europe: uncertainties of scenarios based on regional climate models

Observations as well as most climate model simulations are generally in accord with the hypothesis that the hydrologic cycle should intensify and become highly volatile with the greenhouse-gas-induced climate change, although uncertainties of these projections as well as the spatial and seasonal variability of the changes are much larger than for temperature extremes. In this study, we examine scenarios of changes in extreme precipitation events in 24 future climate runs of ten regional climate models, focusing on a specific area of the Czech Republic (central Europe) where complex orography and an interaction of other factors governing the occurrence of heavy precipitation events result in patterns that cannot be captured by global models. The peaks-over-threshold analysis with increasing threshold censoring is applied to estimate multi-year return levels of daily rainfall amounts. Uncertainties in scenarios of changes for the late 21st century related to the inter-model and within-ensemble variability and the use of the SRES-A2 and SRES-B2 greenhouse gas emission scenarios are evaluated. The results show that heavy precipitation events are likely to increase in severity in winter and (with less agreement among models) also in summer. The inter-model and intra-model variability and related uncertainties in the pattern and magnitude of the change is large, but the scenarios tend to agree with precipitation trends recently observed in the area, which may strengthen their credibility. In most scenario runs, the projected change in extreme precipitation in summer is of the opposite sign than a change in mean seasonal totals, the latter pointing towards generally drier conditions in summer. A combination of enhanced heavy precipitation amounts and reduced water infiltration capabilities of a dry soil may severely increase peak river discharges and flood-related risks in this region.     

Future evolution of extreme precipitation in the Mediterranean

Mediterranean basins can be impacted by severe floods caused by extreme rainfall, and there is a growing awareness about the possible increase in these heavy rainfall events due to climate change. In this study, the climate change impacts on extreme daily precipitation in 102 catchments covering the whole Mediterranean basin are investigated using nonstationary extreme value model applied to annual maximum precipitation in an ensemble of high-resolution regional climate model (RCM) simulations from the Euro-CORDEX experiment. Results indicate contrasted trends, with significant increasing trends in Northern catchments and conversely decreasing trends in Southern catchments. For most cases, the time of signal emergence for these trends is before the year 2000. The same spatial pattern is obtained under the two climate scenarios considered (RCP4.5 and RCP8.5) and in most RCM simulations, suggesting a robust climate change signal. The strongest multi-model agreement concerns the positive trends, which can exceed +?20% by the end of the twenty-first century in some simulations, impacting South France, North Italy, and the Balkans. For these areas, society-relevant strong impacts of such Mediterranean extreme precipitation changes could be expected in particular concerning flood-related damages.     

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.     

Snowpack and runoff response to climate change in Owens Valley and Mono Lake watersheds

Precipitation from the Eastern Sierra Nevada watersheds of Owens Lake and Mono Lake is one of the main water sources for Los Angeles’ over 4 million people, and plays a major role in the ecology of Mono Lake and of these watersheds. We use the Variable Infiltration Capacity (VIC) hydrologic model at daily time scale, forced by climate projections from 16 global climate models under greenhouse gas emissions scenarios B1 and A2, to evaluate likely hydrologic responses in these watersheds for 1950–2099. Comparing climate in the latter half of the 20th Century to projections for 2070–2099, we find that all projections indicate continued temperature increases, by 2–5 °C, but differ on precipitation changes, ranging from ?24 % to +56 %. As a result, the fraction of precipitation falling as rain is projected to increase, from a historical 0.19 to a range of 0.26–0.52 (depending on the GCM and emission scenario), leading to earlier timing of the annual hydrograph’s center, by a range of 9–37 days. Snowpack accumulation depends on temperature and even more strongly on precipitation due to the high elevation of these watersheds (reaching 4,000 m), and projected changes for April 1 snow water equivalent range from ?67 % to +9 %. We characterize the watershed’s hydrologic response using variables integrated in space over the entire simulated area and aggregated in time over 30-year periods. We show that from the complex dynamics acting at fine time scales (seasonal and sub-seasonal) simple dynamics emerge at this multi-year time scale. Of particular interest are the dynamic effects of temperature. Warming anticipates hydrograph timing, by raising the fraction of precipitation falling as rain, reducing the volume of snowmelt, and initiating snowmelt earlier. This timing shift results in the depletion of soil moisture in summer, when potential evapotranspiration is highest. Summer evapotranspiration losses are limited by soil moisture availability, and as a result the watershed’s water balance at the annual and longer scales is insensitive to warming. Mean annual runoff changes at base-of-mountain stations are thus strongly determined by precipitation changes.     

Extreme precipitation over the Maritime Alps and associated weather regimes simulated by a regional climate model: Present-day and future climate scenarios

Summary We use the regional climate model RegCM nested within time-slice atmospheric general circulation model experiments to investigate the possible changes of intense and extreme precipitation over the French Maritime Alps in response to global climate change. This is a region with complex orography where heavy and/or extended precipitation episodes induced catastrophic floods during the last decades. Output from a 30-year simulation of present-day climate (1961–1990) is first analysed and compared with NCEP reanalysed 700 hPa geopotential heights (Z700) and daily precipitation observations from the Alpine Precipitation Climatology (1966–1999). Two simulations under forcing from the A2 and B2 IPCC emission scenarios for the period 2071–2100 are used to investigate projected changes in extreme precipitation for our region of interest. In general, the model overestimates the annual cycle of precipitation. The climate change projections show some increase of precipitation, mostly outside the warm period for the B2 scenario, and some increase in the variability of the annual precipitation totals for the A2 scenario. The model reproduces the main observed patterns of the spatial leading EOFs in the Z700 field over the Atlantic-European domain. The simulated large scale circulation (LSC) variability does not differ significantly from that of the reanalysis data provided the EOFs are computed on the same domain. Two similar clusters of LSC corresponding to heavy precipitation days were identified for both simulated and observed data and their patterns do not change significantly in the climate change scenarios. The analysis of frequency histograms of extreme indices shows that the control simulation systematically underestimates the observed heavy precipitation expressed as the 90^th percentile of rainday amounts in all seasons except summer and better reproduces the greatest 5-day precipitation accumulation. The main hydrological changes projected for the Maritime Alps consist of an increase of most intense wet spell precipitation during winters for both scenarios and during autumn for the B2 scenario. Case studies of heavy precipitation events show that the RegCM is capable to reproduce the physical mechanisms responsible for heavy precipitation over our region of interest.     

Effect of climate change on regional precipitation in Lake Balaton watershed

Summary A methodology is developed and applied to the area of Lake Balaton and its drainage basin, a region of Western Hungary, to estimate the space-time distribution of daily precipitation under climate change. Lake Balaton is the largest lake in Central and Western Europe; it has a central location in the country and its drainage basin covers about the 20% of Hungary (together with the Sió Canal). The methodology is based on an analysis of the semi-Markovian properties of atmospheric macrocirculation pattern types (MCP), and a stochastic linkage between daily (here 700 hPa) MCP types and daily precipitation events. Historical data and General Circulation Model (GCM) output of daily MCP corresponding to 1 · CO₂ and 2 · CO₂ scenarios are considered in this study. Time series of both local and areal precipitation corresponding for both scenarios are simulated and their statistical properties are compared. For the temperate continental climate of Western Hungary a slightly variable spatial response to climate change is obtained. Under 2 · CO₂ conditions most of the local and the areal average precipitation suggests, a somewhat dryer precipitation regime in Western Hungary. The sensitivity of the results to the GCM utilized should be considered.With 10 Figures     

Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO<Subscript>2</Subscript> warming

Increases in extreme precipitation greater than in the mean under increased greenhouse gases have been reported in many climate models both on global and regional scales. It has been proposed in a previous study that whereas global-mean precipitation change is primarily constrained by the global energy budget, the heaviest events can be expected when effectively all the moisture in a volume of air is precipitated out, suggesting the intensity of these events increases with availability of moisture, and significantly faster than the global mean. Thus under conditions of constant relative humidity one might expect the Clausius–Clapeyron relation to give a constraint on changes in the uppermost quantiles of precipitation distributions. This study examines if the phenomenon manifests on regional and seasonal scales also. Zonal analysis of daily precipitation in the HadCM3 model under a transient CO₂ forcing scenario shows increased extreme precipitation in the tropics accompanied by increased drying at lower percentiles. At mid- to high-latitudes there is increased precipitation over all percentiles. The greatest agreement with Clausius–Clapeyron predicted change occurs at mid-latitudes. This pattern is consistent with other climate model projections, and suggests that regions in which the nature of the ambient flows change little give the greatest agreement with Clausius–Clapeyron prediction. This is borne out by repeating the analyses at gridbox level and over season. Furthermore, it is found that Clausius–Clapeyron predicted change in extreme precipitation is a better predictor than directly using the change in mean precipitation, particularly between 60°N and 60°S. This could explain why extreme precipitation changes may be more detectable then mean changes.     

1957—2007年雅鲁藏布江中游河谷降水变化的小波分析

通过对近51 a雅鲁藏布江中游河谷地区4个气象站逐日降水资料进行等权平均取值,采用气候倾向率和小波分析法,研究了不同时间尺度下近51 a降水变化趋势及周期特征。结果表明:研究区降水量以20世纪80年代为最少,2000年后年降水量与20世纪90年代和60年代基本持平。夏季和秋季降水量年代际变化与年降水量的变化基本一致。近51 a降水量增加趋势不显著。年降水量存在准3 a、8—11 a和30 a的周期,以准11 a周期最为突出。降水量变化以春季增长趋势最显著,可在干旱季补充土壤水分,减轻风沙化土地的发生发展。秋季和冬季增长趋势不明显,夏季降水量呈减小趋势。就季节降水变化的时间尺度和周期性而言,2007年后四季降水表现为,春季在20—30 a时间尺度上将处于偏高期,夏季在8—12 a时间尺度上将处于偏低期,秋季在8—12 a时间尺度上将处于偏低期,冬季在20—30 a时间尺度上将出现向降水偏高的过渡期。     

华北地区小时极端降水的非稳态研究

本文以华北五省为研究区，基于1960—2014年小时降水数据建立1、2、3、6、12和24 h极端降水序列，对比分析稳态和非稳态假设下极端降水重现期估计的差异。研究表明：1960―2014年华北不同时间极端降水的变化趋势略有不同，时间越短呈上升趋势的站点越多，1~3 h的极端降水呈上升趋势的站点较多，稳态和非稳态假设下的20~100 a一遇重现期平均差异较大，其中，1 h极端降水的显著上升站点中，二者的平均相对误差达30%~43%；而6~24 h极端降水中，呈下降趋势的站点增多，其中，24 h极端降水显著下降站点中，二者的平均相对误差达-43%~-32%；无显著趋势站点，二者的平均相对误差大部分介于-10%~10%。随着重现期增大，二者差异的不确定性区间增大，不同变化趋势站点表现一致。研究发现，华北地区短历时极端降水强度增加，稳态假设下极端降水的重现期会严重低估。因此，选用非稳态假设估计极端降水的重现期，将降低极端降水的灾害风险。     

RCPs情景下汉江流域未来极端降水的模拟与预估

采用应用于跨行业影响模式比较计划(ISIMIP)的5个CMIP5全球气候模式模拟的历史和未来RCP排放情景下的逐日降水数据,在评估模式对汉江流域1961—2005年极端降水变化特征模拟能力的基础上,进一步计算了RCP2.6、RCP4.5和RCP8.5排放情景下汉江流域未来2016—2060年极端降水总量(R95p)、极端降水贡献率(PEP)、连续5 d最大降水(RX5d)和降水强度(SDII),结果表明:RCP4.5情景下的极端降水指数上升最明显,R95p和RX5d分别较基准期增加12.5%和8.2%,PEP增加3.2个百分点,SDII微弱上升。在不同排放情景下,PEP均有一定的增幅,以流域西北和东南部增幅较大;R95p在流域绝大部分区域表现出一定的增加,且流域东南部和北部是增幅高值区;RX5d在RCP2.6和RCP4.5情景下整体表现为增加的特征,但在RCP8.5情景下整体表现为减少的特征。对极端降水预估的不确定性中,SDII的不确定性最小,RX5d的不确定性最大;不确定性大值区主要位于流域东部、东南部和西北部部分区域。     

基于降水极端预报指数的福建台风极端降水预报研究

应用1961—2017年中国气象局热带气旋最佳路径数据集、国家地面气象观测站日降水观测资料和2015年8月—2017年12月欧洲中期天气预报中心（ECMWF）集合预报系统降水极端预报指数（EFI）数据,根据百分位法定义台风影响期间福建省各站点的台风极端降水阈值,采用最小阈值法剔除台风极端降水时EFI箱线图中的异常值,保留最小值作为台风极端降水EFI阈值,建立基于EFI阈值的台风极端降水预报方法。用该方法分别对2015年8月—2017年12月和2018年登陆或影响福建台风进行台风极端降水回报和预报试验,采用TS、空报率、漏报率对回报和预报结果进行检验。结果表明:福建台风极端降水阈值由沿海向西北内陆逐渐减小,其中中北部沿海地区的阈值最大;台风日降水量与日降水EFI预报、台风极端降水时的日降水量与日降水EFI预报均存在明显的正相关,日降水EFI预报的箱线图差异指数（I_bd）也表明EFI可以较好地区分台风极端降水和非极端降水;预报试验20时（北京时）起报时效12—36、36—60、60—84和84—108 h的台风极端降水预报TS分别达到0.26、0.22、0.20和0.19,总体上略优于回报试验;台风极端降水越显著,台风极端降水预报效果越好,回报和预报都存在的不足是空报率高,主要出现在台风极端降水不明显的个例中。降水EFI预报对台风极端降水预报具有较好的指示意义,基于降水EFI阈值的台风极端降水预报产品可在业务中作为台风极端降水预报的参考。      

Climate change scenarios for precipitation extremes in Portugal

Precipitation indices are commonly used as climate change indicators. Considering four Climate Variability and Predictability-recommended indices, this study assesses possible changes in their spatial patterns over Portugal under future climatic conditions. Precipitation data from the regional climate model Consortium for Small-Scale Modelling–Climate version of the Local Model (CCLM) ensemble simulations with ECHAM5/MPI-OM1 boundary conditions are used for this purpose. For recent–past, medians and probability density functions of the CCLM-based indices are validated against station-based and gridded observational dataset from ENSEMBLES-based (gridded daily precipitation data provided by the European Climate Assessment & Dataset project) indices. It is demonstrated that the model is able to realistically reproduce not only precipitation but also the corresponding extreme indices. Climate change projections for 2071–2100 (A1B and B1 SRES scenarios) reveal significant decreases in total precipitation, particularly in autumn over northwestern and southern Portugal, though changes exhibit distinct local and seasonal patterns and are typically stronger for A1B than for B1. The increase in winter precipitation over northeastern Portugal in A1B is the most important exception to the overall drying trend. Contributions of extreme precipitation events to total precipitation are also expected to increase, mainly in winter and spring over northeastern Portugal. Strong projected increases in the dry spell lengths in autumn and spring are also noteworthy, giving evidence for an extension of the dry season from summer to spring and autumn. Although no coupling analysis is undertaken, these changes are qualitatively related to modifications in the large-scale circulation over the Euro-Atlantic area, more specifically to shifts in the position of the Azores High and associated changes in the large-scale pressure gradient over the area.     

Spatial and temporal characteristics of intraseasonal oscillations of precipitation over the United States

Summary  This study shows that precipitation over the United States has two time scales of intraseasonal variation at about 37 days and 24 days. The results are derived from the application of a combination of statistical methods including principal component analysis (PCA), singular spectrum analysis (SSA), and multi-channel singular spectrum analysis (MSSA) to over 10 years of gridded daily precipitation records. Both oscillations have largest amplitude during the cold season. The 37-day oscillation has larger interannual variability. Intraseasonal oscillations are most significant in the Pacific Northwest. The 37-day oscillation has opposite phases between the western and eastern United States, while the 24-day oscillation has the same phases. These intraseasonal time scale precipitation variations may be associated with previously revealed mid-tropospheric circulation anomalies that oscillate at similar time scales. Received February 7, 2000 Revised October 20, 2000     

南京过去100年极端日降水量模拟研究

在南京过去100年日降水资料的基础上,利用极值理论中的区组模型和阈值模型分析了极端日降水分布特征.首先通过广义极值(GEV)模型模拟了日降水的年极值序列(AMDR),用极大似然估计(MLE)方法计算了模型的参数,并借助轮廓似然函数估计出参数的精确误差区间,同时采用4种较直观的诊断图形对模型的合理性进行全面评估,结果表明Frechet是区组模型中最适合描述极端日降水分布特征的函数.其次,将日降水序列分3种情景构建极值分布的阈值模型(GPD),考察了观测数据的规模对应用该模型的限制,重点讨论了如何针对给定观测样本选择合适的阈值收集极值信息.分析结果认为,长度不小于50年的气候序列,采用24 mm的日降水量作为临界阈值均能进行GPD分析.该阈值处于年降水序列第91个百分位附近,即对目前长度为50年左右的日观测资料,第91个百分位点以上的数据基本能满足GPD研究的需要.另外,根据GEV和GPD对未来极端降水重现水平的推断情况,GPD预测值的置信区间要比GEV的窄,极值推断的不确定性相对也较小,更适合用于研究中国目前规模不大的气候资料.最后,对GPD模型的形状参数和尺度参数进行变换,分别引入描述线性变化的动态变量,分析降水序列中潜在的变异行为对极值理论应用的影响.这种变异包括降水序列中长期的均值变化及百分位变化,从模拟结果看,暂未发现资料变异行为对极值分析产生显著于扰.     

气候变化对水文季节性迁移的影响：以水库汛期分期和极值降水为例

气候变化影响下水利工程的可靠设计和安全运行是广大决策者、研究者和公众共同关注的热点问题。以清江流域为研究对象,首先采用模糊集合分析法对不同温室气体排放情景(A2、A1B和B1)下的逐日降水资料进行汛期分期,再通过广义极值分布(GEV)函数对各分期的极值降水序列进行频率分析。结果表明,降水季节性迁移直接影响汛期分期;3种排放情景下未来各时段(2011—2030年、2046—2065年和2080—2099年)的主汛期较基准期均推迟且有缩短趋势。对于极值降水量级,未来情景下明显小于基准期,且这种差距随着重现期的增大而增大;主汛期明显大于前汛期和后汛期,且在时段之间的差异明显大于排放情景之间的差异。     

Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections

It is well accepted within the scientific community that a large ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a state-of-the-art multi-model multi-scenario ensemble of global and regional precipitation projections. This ensemble combines several global projections from the CMIP3 and CMIP5 databases, along with some recently downscaled regional CORDEX-Africa projections. Altogether daily precipitation data from 77 different climate change projections is analysed; separated into 31 projections for a high and 46 for a low emission scenario. We find a robust indication that, independent of the underlying emission scenario, annual total precipitation amounts over the central African region are not likely to change severely in the future. However some robust changes in precipitation characteristics, like the intensification of heavy rainfall events as well as an increase in the number of dry spells during the rainy season are projected for the future. Further analysis shows that over some regions the results of the climate change assessment clearly depend on the size of the analyzed ensemble. This indicates the need of a “large-enough” ensemble of independent climate projections to allow for a reliable climate change assessment.     

Drivers of mean climate change around the Netherlands derived from CMIP5

For the construction of regional climate change scenarios spanning a relevant fraction of the spread in climate model projections, an inventory of major drivers of regional climate change is needed. For the Netherlands, a previous set of regional climate change scenarios was based on the decomposition of local temperature/precipitation changes into components directly linked to the level of global warming, and components related to changes in the regional atmospheric circulation. In this study this decomposition is revisited utilizing the extensive modelling results from the CMIP5 model ensemble in support for the 5th IPCC assessment. Rather than selecting a number of GCMs based on performance metrics or relevant response features, a regression technique was developed to utilize all available model projections. The large number of projections allows a quantification of the separate contributions of emission scenarios, systematic model responses and natural variability to the total likelihood range. Natural variability plays a minor role in modelled differences in the global mean temperature response, but contributes for up to 50 % to the range of mean sea level pressure responses and local precipitation. Using key indicators (“steering variables”) for the temperature and circulation response, the range in local seasonal mean temperature and precipitation responses can be fairly well reproduced.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.