Prediction Of Summer Precipitation Research Articles

Relationship between Tropical Indian Ocean SSTA in Spring and Precipitation of Northeast China in Late Summer

Sea surface temperature anomaly (SSTA) is a major signal for prediction of summer precipitation in East Asia. The relationship between SSTA in the tropical oceans and summer precipitation in East Asia has been documented in many studies. However, the relationship between SSTA and late summer (July-August) precipitation (JAP) over Northeast China (NEC) on the interannual timescale has received little attention. In this study, we examine the relationship between Indian Ocean Basin warming (IOBW) anomalies in spring and the JAP in NEC since the early 1960s. A significant positive correlation is found between the spring IOBW index and JAP over NEC. The positive spring IOBW anomaly is followed by an anomalous anticyclone from Northwest Pacific to the Korean Peninsula. This anomalous anticyclone favors a significantly strong and northward western Pacific subtropical high (WPSH), which facilitates anomalous southerly winds over NEC and the transport of more water vapor into this region. Further analysis indicates that the spring IOBW anomalies have important impacts on the vertical air motion in the tropics and subtropics during the summer. Significant anomalous upward (downward) motion covering Indonesia (Northwest Pacific to the southern Korean Peninsula) occurs when the IOBW is in its positive phase, which favors the northward movement of the WPSH in late summer and more precipitation over NEC in July-August. Modulation of the atmospheric circulation by this mechanism further influences the JAP over NEC.

Predictive Skill and Predictable Patterns of the U.S. Seasonal Precipitation in CFSv2 Reforecasts of 60 Years (1958–2017)

Abstract Analyzing a set of ensemble seasonal reforecasts for 1958–2017 using CFSv2, we evaluate the predictive skill of the U.S. seasonal mean precipitation and examine its sources of predictability. Our analysis is for each of the three periods of 1958–78, 1979–99, and 2000–17, corresponding to the positive phase of the Pacific decadal oscillation (PDO) during 1979–99 and negative ones before and after. The ensemble reforecasts at two-month lead reproduce the spatial distribution of winter precipitation trends throughout the 60 years and the continental-scale increase of summer precipitation since 2000. The predicted signal-to-noise (S/N) ratio also reveals greater predictability in the post-1979 period than during 1958–78. A maximized S/N ratio EOF analysis is applied to the ensemble seasonal precipitation predictions. In winter and spring, the most predictable patterns feature a north–south dipole throughout United States. The summer and fall patterns are dominated by the anomalies in central and southern United States, respectively. In verification with observations, the winter–spring patterns are more skillful. ENSO influences on these predictable patterns are most dominant in winter and spring, but other oceanic factors also play an active role during summer and fall. The multidecadal change of the U.S. precipitation predictability is attributable to the low-frequency modulation of the ENSO predictability and the influences of other major climate modes. PDO can be a dominant factor associated with enhanced prediction skill in 1979–99 and reduced skill in 1958–78. Since the 2000s, the forcing from the SST anomalies in the tropical North Atlantic with opposite sign to those in the tropical Pacific becomes a significant factor for the U.S. summer precipitation prediction.

Multi-scheme corrected dynamic–analogue prediction of summer precipitation in northeastern China based on BCC_CSM

Based on summer precipitation hindcasts for 1991–2013 produced by the Beijing Climate Center Climate System Model (BCC_CSM), the relationship between precipitation prediction error in northeastern China (NEC) and global sea surface temperature is analyzed, and dynamic–analogue prediction is carried out to improve the summer precipitation prediction skill of BCC_CSM, through taking care of model historical analogue prediction error in the real-time output. Seven correction schemes such as the systematic bias correction, pure statistical correction, dynamic–analogue correction, and so on, are designed and compared. Independent hindcast results show that the 5-yr average anomaly correlation coefficient (ACC) of summer precipitation is respectively improved from –0.13/0.15 to 0.16/0.24 for 2009–13/1991–95 when using the equally weighted dynamic–analogue correction in the BCC_CSM prediction, which takes the arithmetical mean of the correction based on regional average error and that on grid point error. In addition, probabilistic prediction using the results from the multiple correction schemes is also performed and it leads to further improved 5-yr average prediction accuracy.

Assessment and correction of BCC_CSM's performance in capturing leading modes of summer precipitation over North Asia

ABSTRACTThis article examines the ability of Beijing Climate Center Climate System Model (BCC_CSM) in demonstrating the prediction accuracy and the leading modes of the summer precipitation over North Asia (NA). A dynamic‐statistic combined approach for improving the prediction accuracy and the prediction of the leading modes of the summer precipitation over NA is proposed. Our results show that the BCC_CSM can capture part of the spatial anomaly features of the first two leading modes of NA summer precipitation. Moreover, BCC_CSM regains relationships such that the first and second mode of the empirical orthogonal function (EOF1 and EOF2) of NA summer precipitation, respectively, corresponds to the development of the El Niño and La Niña conditions in the tropical East Pacific. Nevertheless, BCC_CSM exhibits limited prediction skill over most part of NA and presents a deficiency in reproducing the EOF1's and EOF2's spatial pattern over central NA and EOF2's interannual variability. This can be attributed as the possible reasons why the model is unable to capture the correct relationships among the basic climate elements over the central NA, lacks in its ability to reproduce a consistent zonal atmospheric pattern over NA, and has bias in predicting the relevant Sea Surface Temperature (SST) modes over the tropical Pacific and Indian Ocean regions. Based on the proposed dynamic‐statistic combined correction approach, compared with the leading modes of BCC_CSM's original prediction, anomaly correlation coefficients of corrected EOF1/EOF2 with the tropical Indian Ocean SST are improved from 0.18/0.36 to 0.51/0.62. Hence, the proposed correction approach suggests that the BCC_CSM's prediction skill for the summer precipitation prediction over NA and its ability to capture the dominant modes could be certainly improved by choosing proper historical analogue information.

Predictability and prediction of summer rainfall in the arid and semi-arid regions of China

Northwest China (NWC) is an arid and semi-arid region where climate variability and environmental changes are sensitive to precipitation. The present study explores sources and limits of predictability of summer precipitation over NWC using the predictable mode analysis (PMA) of percentage of rainfall anomaly data. Two major modes of NWC summer rainfall variability are identified which are tied to Eurasian continental scale precipitation variations. The first mode features wet northern China corresponding to dry central Siberia and wet Mongolia, which is mainly driven by tropical Pacific sea surface temperature anomalies (SSTA). The second mode features wet western China reflecting wet Central Asia and dry Ural–western Siberia, which strongly links to Indian Ocean SSTA. Anomalous land warming over Eurasia also provides important precursors for the two modes. The cross-validated hindcast results demonstrate these modes can be predicted with significant correlation skills, suggesting that they may be considered as predictable modes. The domain averaged temporal correlation coefficient (TCC) skill during 1979 to 2015 using 0-month (1-month) lead models is 0.39 (0.35), which is considerably higher than dynamical models’ multi-model ensemble mean skill (−0.02). Maximum potential attainable prediction skills are also estimated and discussed. The result illustrates advantage of PMA in predicting rainfall over dry land areas and large room for dynamical model improvement. However, secular changes of predictors need to be detected continuously in order to make practical useful prediction.

Methods for Improving the Prediction Skill of Summer Precipitation over East Asia–West Pacific

AbstractThe performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model (BCC_CSM1.1; hereafter CSM) over the East Asia–west Pacific (EA–WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistical alone prediction and the statistical and model combined prediction, are proposed to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model; both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analog information and the CSM’s forecast are considered in the predictive model; this new prediction method is based on merging information from both a dynamical model and historical analogs. Cross validation of summer precipitation over EA–WP for the years 1991–2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE results from the statistical and CSM combined prediction are 0.22 and 21.8, respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the years 2011–13 indicates that the average ACCs of the statistical alone prediction and the statistical and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (−0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for the operational prediction of summer precipitation over EA–WP.

Summer precipitation prediction in the source region of the Yellow River using climate indices

The summer precipitation from June to September in the source region of the Yellow River accounts for about 70% of the annual total, and its decrease would cause further water shortage problems. Consequently, the objectives of this study are to improve the understanding of the linkages between the precipitation in the source region of the Yellow River and global teleconnection patterns, and to predict the summer precipitation based on revealed teleconnections. Spatial variability of precipitation was investigated based on three homogeneous sub-regions. Principal component analysis and singular value decomposition were used to find significant relationships between the precipitation and global teleconnection patterns using climate indices. A back-propagation neural network was developed to predict the summer precipitation using significantly correlated climate indices. It was found that precipitation in the study area is positively related to North Atlantic Oscillation, West Pacific Pattern and El Niño Southern Oscillation, and inversely related to Polar Eurasian pattern. Summer precipitation was overall well predicted. The Pearson correlation coefficient between predicted and observed summer precipitation was, in general, larger than 0.6. The results can be used to predict the summer precipitation and to improve integrated water resources management in the Yellow River basin.

Extreme precipitation events over north China in August 2010 and their link to eastward‐propagating wave‐trains across Eurasia: observations and monthly forecasting

Over the Far East in summer, climate is strongly influenced by the fluctuating Western Pacific Subtropical High (WPSH), and strong precipitation is often associated with southeasterly low‐level wind that brings moist air from the southern China seas. The WPSH intraseasonal variability is partly influenced by quasi‐stationary wave‐trains propagating eastwards from Europe across Asia along the two westerly jets: the Silk‐Road wave‐train along the Asian jet at midlatitudes and the polar wave‐train along the sub‐polar jet. In the unusual summer of 2010, northeast China experienced its worst seasonal flooding for a decade, triggered by unusually severe precipitation. That summer was also characterized by a record‐breaking heat wave over eastern Europe and Russia, whose impact on the precipitation further east over China has been little explored. Here, we examine the role of the Silk‐Road and polar wave‐trains, and their impact on precipitation over northeast China throughout August 2010, using station precipitation data and re‐analyses. We found that there is a strong link between the Silk‐Road wave‐train and extreme precipitation events.Forecasting such regional precipitation events at the monthly time‐scale remains a big challenge for operational global prediction systems. In this study, we use simulations with the atmospheric model of the European Centre for Medium‐range Weather Forecasts (ECMWF) to highlight the key role of the Silk‐Road and polar wave‐trains in modulating extreme precipitation over north and northeast China in August 2010. While the ensemble‐mean of the forecasts fails to predict the pulses of the Silk‐Road wave‐train, some model members show a high spatial correlation in upper‐level meridional winds with re‐analyses. Similarly, there is high spatial correlation between model meridional winds and precipitation. These results highlight the importance of better representing the intraseasonal evolution of the Silk‐Road wave‐train in order to improve the monthly prediction of summer precipitation over the Far East.

Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models

在 Huaihe 河盆上的季节的降雨可预测性在这份报纸被评估根据 23 年(1981-2003 ) 由从亚太经济合作(APEC ) 气候的 10 个气候模型的回顾的预报集中(APCC ) 多模型整体(MME ) 预言系统。在这盆的夏天降雨变化大部分是内部的，这被发现，它为很单个的气候模型导致更低的降雨可预测性。由划分， 10 根据他们包括的边界条件观察了的海表面温度(SST ) 当模特儿进三个范畴，预言，并且坚持的 SST，在 Huaihe 河盆上的夏天降雨的 MME 确定的预兆的技巧被调查。MME 为增加当前的季节的预报技巧是有效的，这被显示出。MME 平均在上的进一步的分析表演预言了 SST 模型在在 Huaihe 复制夏天降雨异例的观察主导的模式与模型的能力有关仔细有最高的降雨预言技巧，它是河盆。这结果能进一步被归功于到预言的 SST MME 是为在赤道的海洋在 Huaihe 河盆和 SST 异例(SSTA ) 上捕获在夏天降雨异例之间的关系的最有效的模型整体的事实。

Dynamic downscaling of summer precipitation prediction over China in 1998 using WRF and CCSM4

To study the prediction of the anomalous precipitation and general circulation for the summer (June–July–August) of 1998, the Community Climate System Model Version 4.0 (CCSM4.0) integrations were used to drive version 3.2 of the Weather Research and Forecasting (WRF3.2) regional climate model to produce hindcasts at 60 km resolution. The results showed that the WRF model produced improved summer precipitation simulations. The systematic errors in the east of the Tibetan Plateau were removed, while in North China and Northeast China the systematic errors still existed. The improvements in summer precipitation interannual increment prediction also had regional characteristics. There was a marked improvement over the south of the Yangtze River basin and South China, but no obvious improvement over North China and Northeast China. Further analysis showed that the improvement was present not only for the seasonal mean precipitation, but also on a sub-seasonal timescale. The two occurrences of the Mei-yu rainfall agreed better with the observations in the WRF model, but were not resolved in CCSM. These improvements resulted from both the higher resolution and better topography of the WRF model.

Using a deterministic time-lagged ensemble forecast with a probabilistic threshold for improving 6–15 day summer precipitation prediction in China

A Deterministic Time-lagged Ensemble Forecast using a Probabilistic Threshold (DEFPT) method is suggested for improving summer 6–15day categorical precipitation prediction in China from the Beijing Climate Center Atmospheric General Circulation Model version 2.1 (BCC_AGCM2.1). It is based on a time-lagged ensemble system that consists of 13 ensemble members separated sequentially at 6hour intervals lagging the last three days. The DEFPT is not intended to predict the probability of rainfall, but rather to forecast rainfall (yes/no) occurrence for different categories of precipitation at any model grid box. A given categorical precipitation is forecasted to occur at one gridbox only when the ensemble probability for that categorical precipitation exceeds a certain threshold. This method is useful for providing an estimate of whether precipitation events will occur to decision-makers based on probabilistic forecasts during days 6–15. A large number of hindcast experiments for 1996–2005 summers reveal that this threshold can be best (and empirically) set as 5/13 and 4/13 respectively for the 6–15day prediction of 1+ mm (i.e., above 1mm per day) and 5+ mm rainfall events, using the Relative Operating Characteristic (ROC) curve, the Equitable Threat Score (ETS), the Hanssen and Kuipers (HK) score, and frequency bias (BIA) to achieve best prediction performance. With this set of thresholds, the DEFPT shows skill improvement over the corresponding single deterministic forecast using one initial value and the Time-Lagged Average Forecast (LAF) ensemble method. Similar improvements by the DEFPT are also found for the prediction of several other categories of precipitation between 1+ mm and 10+ mm per day. Application of DEFPT to larger ensemble size and BCC_AGCM version 2.2 with a higher horizontal resolution also demonstrates the effectiveness of the DEFPT for 6–15day categorical precipitation forecasts.

Changed relationships between the East Asian summer monsoon circulations and the summer rainfall in eastern China

In previous statistical forecast models, prediction of summer precipitation along the Yangtze River valley and in North China relies heavily on its close relationships with the western Pacific subtropical high (WPSH), the blocking high in higher latitudes, and the East Asian summer monsoon (EASM). These relationships were stable before the 1990s but have changed remarkably in the recent two decades. Before the 1990s, precipitation along the Yangtze River had a significant positive correlation with the intensity of the WPSH, but the correlation weakened rapidly after 1990, and the correlation between summer rainfall in North China and the WPSH also changed from weak negative to significantly positive. The changed relationships present a big challenge to the application of traditional statistical seasonal prediction models. Our study indicates that the change could be attributed to expansion of the WPSH after around 1990. Owing to global warming, increased sea surface temperatures in the western Pacific rendered the WPSH stronger and further westward. Under this condition, more moisture was transported from southern to northern China, leading to divergence and reduced (increased) rainfall over the Yangtze River (North China). On the other hand, when the WPSH was weaker, it stayed close to its climatological position (rather than more eastward), and the circulations showed an asymmetrical feature between the stronger and weaker WPSH cases owing to the decadal enhancement of the WPSH. Composite analysis reveals that the maximum difference in the moisture transport before and after 1990 appeared over the western Pacific. This asymmetric influence is possibly the reason why the previous relationships between monsoon circulations and summer rainfall have now changed.

A possible link between wildfire aerosol and North American Monsoon precipitation in Arizona–New Mexico

ABSTRACTPrevious research highlights the dominant role of Pacific sea surface temperatures (SSTs) and their associated large‐scale teleconnections in modulating the North American monsoon (NAM). At the regional scale, feedbacks associated with land‐surface boundary conditions have been shown to provide ‘memory’ in the system. Here, a previously unexplored second‐order linkage between aerosol generated by late‐spring wildfires and subsequent summer precipitation delivered by the NAM in the Arizona‐New Mexico (AZNM) region is proposed. Correlations between June/July organic carbon and elemental carbon (OC/EC) in Inter‐agency Monitoring of Protected Visual Environments (IMPROVE) aerosol data (Gila Wilderness and Bandelier) and NAM precipitation in southern AZNM over the period 1994–2012 are shown to be negative (r = –0.4), suggesting that active antecedent wildfire seasons tend to be immediately followed by a weak late summer monsoon. This result is consistent with a previous study linking precipitation and area burned in wildfires in AZNM. A survey of extant literature suggests a sound basis for potential mechanisms related to convective processes and cloud microphysics, and furthermore suggests that this forcing could be of similar magnitude to well‐documented land‐based second‐order NAM forcings (antecedent snowpack and soil moisture, vegetation, and mineral dust). Based on these results, we believe that the role of aerosols in modulating summer precipitation deserves further investigation both observationally and in modeling studies. If indeed wildfire smoke does contribute to the modulation of NAM intensity, by virtue of its close temporal association with NAM season, it may well represent a factor that could contribute effectively to improved seasonal prediction of summer precipitation in the NAM region.

Predicting US summer precipitation using NCEP Climate Forecast System version 2 initialized by multiple ocean analyses

This study examines the prediction skill of the contiguous United States (CONUS) precipitation in summer, as well as its potential sources using a set of ensemble hindcasts conducted with the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 and initialized from four independent ocean analyses. The multiple ocean ensemble mean (MOCN_ESMEAN) hindcasts start from each April for 26 summers (1982–2007), with each oceanic state paired with four atmosphere-land states. A subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) project for the same period, from the same initial month and with the same total ensemble size, is also analyzed. Compared with CFSRR, MOCN_ESMEAN is distinguished by its oceanic ensemble spread that introduces potentially larger perturbations and better spatial representation of the oceanic uncertainty. The prediction skill of the CONUS precipitation in summer shows a similar spatial pattern in both MOCN_ESMEAN and CFSRR, but the results suggested that initialization from multiple ocean analyses may bring more robust signals and additional skills to the seasonal prediction for both sea surface temperature and precipitation. Among the predictable areas for precipitation, the northwestern CONUS (NWUS) is the most robust. A further analysis shows that the enhanced summer precipitation prediction skill in NWUS is mainly associated with the El Nino/Southern Oscillation, with possible influence also from the Pacific Decadal Oscillation. Through this work, we argue that a large ensemble is necessary for precipitation forecast in mid-latitudes, such as the CONUS, and taking into account of the oceanic initial state uncertainty is an efficient way to build such an ensemble.

Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models

在 Huaihe 河盆上的季节的降雨可预测性在这份报纸被评估根据 23 年(1981-2003 ) 由从亚太经济合作(APEC ) 气候的 10 个气候模型的回顾的预报集中(APCC ) 多模型整体(MME ) 预言系统。在这盆的夏天降雨变化大部分是内部的，这被发现，它为很单个的气候模型导致更低的降雨可预测性。由划分， 10 根据他们包括的边界条件观察了的海表面温度(SST ) 当模特儿进三个范畴，预言，并且坚持的 SST，在 Huaihe 河盆上的夏天降雨的 MME 确定的预兆的技巧被调查。MME 为增加当前的季节的预报技巧是有效的，这被显示出。MME 平均在上的进一步的分析表演预言了 SST 模型在在 Huaihe 复制夏天降雨异例的观察主导的模式与模型的能力有关仔细有最高的降雨预言技巧，它是河盆。这结果能进一步被归功于到预言的 SST MME 是为在赤道的海洋在 Huaihe 河盆和 SST 异例(SSTA ) 上捕获在夏天降雨异例之间的关系的最有效的模型整体的事实。

Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical downscaling model

We attempt to apply year-to-year increment prediction to develop an effective statistical downscaling scheme for summer (JJA, June–July–August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER) project were used to establish this downscaling model. The independent sample test and the cross-validation test show that the downscaling scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.

Development of a downscaling method in China regional summer precipitation prediction

A downscaling method taking into account of precipitation regionalization is developed and used in the regional summer precipitation prediction (RSPP) in China. The downscaling is realized by utilizing the optimal subset regression based on the hindcast data of the Coupled Ocean-Atmosphere General Climate Model of National Climate Center (CGCM/NCC), the historical reanalysis data, and the observations. The data are detrended in order to remove the in∞uence of the interannual variations on the selection of predictors for the RSPP. Optimal predictors are selected through calculation of anomaly correlation coe‐cients (ACCs) twice to ensure that the high-skill areas of the CGCM/NCC are also those of observations, with the ACC value reaching the 0.05 signiflcant level. One-year out cross-validation and independent sample tests indicate that the downscaling method is applicable in the prediction of summer precipitation anomaly across most of China with high and stable accuracy, and is much better than the direct CGCM/NCC prediction. The predictors used in the downscaling method for the RSPP are independent and have strong physical meanings, thus leading to the improvements in the prediction of regional precipitation anomalies.

Statistical Downscaling Prediction of Summer Precipitation in Southeastern China

A statistical downscaling approach based on multiple-linear-regression (MLR) for the prediction of summer precipitation anomaly in southeastern China was established, which was based on the outputs...

A New Scheme for Improving the Seasonal Prediction of Summer Precipitation Anomalies

Abstract A new scheme is developed to improve the seasonal prediction of summer precipitation in the East Asian and western Pacific region. The scheme is applied to the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) results. The new scheme is designed to consider both model predictions and observed spatial patterns of historical “analog years.” In this paper, the anomaly pattern correlation coefficient (ACC) between the prediction and the observation, as well as the root-mean-square error, is used to measure the prediction skill. For the prediction of summer precipitation in East Asia and the western Pacific (0°–40°N, 80°–130°E), the prediction skill for the six model ensemble hindcasts for the years of 1979–2001 was increased to 0.22 by using the new scheme from 0.12 for the original scheme. All models were initiated in May and were composed of nine member predictions, and all showed improvement when applying the new scheme. The skill levels of the predictions for the six models increased from 0.08, 0.08, 0.01, 0.14, −0.07, and 0.07 for the original scheme to 0.11, 0.14, 0.10, 0.22, 0.04, and 0.13, respectively, for the new scheme.

Impact of Model Resolution on the Prediction of Summer Precipitation over the United States and Mexico

Abstract Summer seasonal simulations for selected years were performed using the NCEP Global Forecast System (GFS) at high (T126L28) and low (T62L28) resolutions, and the NCEP 80-km regional spectral model (RSM) nested in the T62 model outputs (RSM80/T62). All models have 28 levels in the vertical. All experiments were performed with prescribed observed sea surface temperatures to ensure that simulation errors came from model deficiencies. While the T126L28 model does not have a statistically significant advantage in simulating 500-hPa height anomalies over the Pacific–North American domain, it yields better monsoon precipitation forecasts and interannual variability. The T62L28 model simulations are too dry over the Southwest and northwestern Mexico when compared to observations and do not properly capture interannual variations of monsoon rainfall. The RSM80/T62 nesting improves the overall rainfall simulations somewhat but is not able to overcome deficiencies of the T62L28 global model to capture interannual variations in monsoon precipitation. Results indicate that a high-resolution version of the global model is needed for seasonal forecasts of monsoon precipitation. Both models capture the low-level jet from the Great Plains (GPLLJ) and rainfall anomalies associated with the 1993 summer floods and the 1988 summer drought, although the simulated rainfall maxima are often weaker and shifted spatially when compared to observations. The impact of horizontal resolution is largely local and is limited to areas over the western region of North America. The T126 model is able to capture the low-level jet from the Gulf of California (GCLLJ), while the T62 model is too coarse to resolve the Gulf of California (GOC). Moisture surges along the GOC are not properly simulated by the T62 model. Overall, the T62 model simulates a very dry Southwest and a weaker monsoon.