Seasonal Precipitation Prediction Research Articles

Understanding Prediction Skill of Seasonal Mean Precipitation over the Tropics

Abstract The connection between the local SST and precipitation (SST–P) correlation and the prediction skill of precipitation on a seasonal time scale is investigated based on seasonal hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). The results demonstrate that there is good correspondence between the two: precipitation skill is generally high only over the regions where SST–P correlation is positive and is low where SST–P correlation is small or weakly negative. This result has fundamental implications for understanding the limits of precipitation predictability on seasonal time scale and helps explain spatial variations in the skill of seasonal mean precipitation. Over the regions where atmospheric variability drives the ocean variability (and consequently the local SST–P correlation is weakly negative), the inherently unpredictable nature of atmospheric variability leads to low predictability for seasonal precipitation. On the other hand, over the regions where slow time scale ocean variability drives the atmosphere (and the local SST–P correlation is large positive), the predictability of seasonal mean precipitation is also high.

Potential seasonal predictability: Comparison between empirical and dynamical model estimates

Methods for estimating potential seasonal predictability from a single realization of daily data are validated against an ensemble of simulations from an atmospheric model driven by observed sea surface temperature, sea ice extent, and greenhouse gas concentration. The methods give surprisingly good estimates of potential predictability of seasonal precipitation despite the fact that the methods assume Gaussian distributions. For temperature, the methods systematically underestimate weather noise variance over land, often by a factor of 2 or more. This bias can be reduced by taking account of precipitation‐induced variability. These conclusions may be model dependent, and hence, confirmation in other models would be of interest. Nevertheless, for the state‐of‐the‐art atmospheric model used in this study, the results strongly support the validity of the single time series approach to estimating potential predictability and enhances our confidence in previous estimates of potential predictability based on observations alone.

Case Study: ENSO Events, Rainfall Variability and the Potential of SOI for the Seasonal Precipitation Predictions in Iran

The studies in recent decades show that many natural disasters such as tropical severe storms, hurricanes development, torrential rain, river flooding, and landslides in some regions of the world and severe droughts and wildfires in other areas are due to El Nino-Southern Oscillation (ENSO). This research aims to contribute to an improved definition of the relation between ENSO and seasonal (autumn and winter) variability of rainfall over Iran. The results show that during autumn, the positive phase of SOI is associated with decrease in the rainfall amount in most part of the country; negative phase of SOI is associated with a significant increase in the rainfall amount. It is also found that, during the winter time when positive phase of SOI is dominant, winter precipitation increases in most areas of the eastern part of the country while at the same time the decreases in the amount of rainfall in other parts is not significant. Moreover, with negative phase of SOI in winter season the amount of rainfall in most areas except south shores of Caspian Sea in the north decreases, so that the decrease of rainfall amount in the eastern part is statistically significant.

Climate diagnostics of three major drought events in the Amazon and illustrations of their seasonal precipitation predictions

AbstractThe Amazon has a well‐defined wet austral summer monsoon and dry winter monsoon precipitation regime and experienced a sequence of drought events in the last 13 years. This study performs a comparative assessment of observed and predicted climate conditions during the three most recent drought events in the Amazon, in 1997–1998, 2004–2005 and 2009–2010, with emphasis on how these events affected the regional monsoon‐like precipitation regime. A century long Negro River level time series at Manaus is investigated, applying extreme values theory for estimating return periods of these major drought events. Possible teleconnections of river levels at Manaus and sea surface temperature at remote regions are explored. Large scale oceanic and atmospheric conditions are investigated to highlight the mechanisms associated with the observed drought conditions, particularly during the dry monsoon season. Satellite estimates are used for diagnosing biomass burning aerosol and discuss possible contributions to the observed precipitation deficits in the 2005 and 2010 drought events during the dry monsoon season. The study is concluded with an analysis of the performance of seasonal precipitation predictions for the dry monsoon seasons of July to September 1998, 2005 and 2010 produced with the operational seasonal prediction system used at the Center for Weather Forecasts and Climate Studies (CPTEC) of the Brazilian National Institute for Space Research (INPE). This system was capable of producing 1 month in advance drought warning for the three investigated events, relevant for helping the government and local population make decisions for reducing drought impacts in the Amazon region. Copyright © 2012 Royal Meteorological Society

Autumn Sea Ice Cover, Winter Northern Hemisphere Annular Mode, and Winter Precipitation in Eurasia

Abstract This paper examines the impacts of the previous autumn sea ice cover (SIC) on the winter Northern Hemisphere annular mode (NAM) and winter precipitation in Eurasia. The coherent variations among the Kara–Laptev autumn SIC, winter NAM, and Eurasian winter precipitation appear after the year 1982, which may prove useful for seasonal prediction of winter precipitation. From a physical point of view, the Kara–Laptev SIC and sea surface temperature (SST) anomalies develop in autumn and remain in winter. Given that winter NAM is characterized by an Arctic–midlatitude seesaw centered over the Barents Sea and Kara–Laptev Seas, it is closely linked to the Arctic forcing that corresponds to the Kara–Laptev sea ice increase (reduction) and the associated surface temperature cooling (warming). Moreover, based on both model simulations and observations, the diminishing Kara–Laptev sea ice does induce positive sea level pressure (SLP) anomalies over high-latitude Eurasia in winter, which is accompanied by a significant surface warming in northern Eurasia and cooling south of the Mediterranean. This surface air temperature (SAT) anomaly pattern facilitates increases of specific humidity in northern Eurasia with a major ridge extending southward along the East Asian coast. As a result, the anomalous Eurasian winter precipitation has a more zonal band structure.

A Comprehensive Assessment of CFS Seasonal Forecasts over the Tropics*

Abstract The 15-member ensemble hindcasts performed with the National Centers for Environmental Prediction Climate Forecast System (CFS) for the period 1981–2005, as well as real-time forecasts for the period 2006–09, are assessed for seasonal prediction skills over the tropics, from deterministic (anomaly correlation), categorical (Heidke skill score), and probabilistic (rank probability skill score) perspectives. Further, persistence, signal-to-noise ratio, and root-mean-square error analyses are also performed. The CFS demonstrates high skill in forecasting El Niño–Southern Oscillation (ENSO) related sea surface temperature (SST) anomalies during developing and mature phases, including that of different types of El Niño. During ENSO, the space–time evolution of anomalous SST, 850-hPa wind, and rainfall along the equatorial Pacific, as well as the mechanisms involved in the teleconnection to the tropical Indian Ocean, are also well represented. During ENSO phase transition and in the summer, the skill of forecasting Pacific SST anomalies is modest. An examination of CFS ability in forecasting seasonal rainfall anomalies over the U.S. Affiliated Pacific Islands (USAPI) indicates that forecasting the persistence of dryness from El Niño winter into the following spring/summer is skillful at leads > 3 months. During strong El Niño years the persistence is predicted by all members with a 6-month lead time. Also, the model is skillful in predicting regional rainfall responses during different types of El Niño. Since both deterministic and probabilistic skill scores converge, the suggestion is that the forecast is useful. The model’s skill in the real-time forecasts for the period 2006–09 is also discussed. The results suggest the feasibility that a dynamical-system-based seasonal prediction of precipitation over the USAPI can be considered.

WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982–2008

The non-hydrostatic Weather Research and Forecasting model (WRF) was nested into NCEP’s operational seasonal forecast model Climate Forecast System (CFS) to downscale seasonal prediction of winter precipitation over continental China. Using the same initial conditions, 16 ensemble downscaling forecasts configured with two alternative schemes of microphysics, cumulus, land surface and radiation in WRF were conducted at 30 km for 27-cold seasons (December–February) during 1982–2008. On average, WRF downscaling forecasts reduced wet bias of seasonal mean precipitation from CFS prediction by 25–71%, decreased errors by up to 33%, and increased equitable threat score by 0.1 for low threshold. With appropriate physical configurations, WRF could improve interannual variations over the region where CFS has correct anomaly signal. The spatial distribution of daily precipitation characteristics such as rainy frequency and extremes highlighted the sensitivity of downscaling forecasts to physical configurations, and the dominant uncertainties were introduced by land surface and radiation schemes. The differences in convective and resolved rainfall between alternative land surface and radiation schemes were consistent with differences of surface downwelling shortwave and longwave radiation through cloud-radiation feedback. Such feedback was strengthened in the land surface sensitivity experiments due to different parameterizations of surface albedo. As compared with CFS ensemble predictions with different initial conditions, the WRF ensemble downscaling forecasts with various physical schemes had larger spread, and some schemes could complement each other in different regions that provided a promising opportunity to enhance the prediction through optimization. The optimized WRF reduced error from the optimized CFS by 30% and increased pattern correlation by 0.12. Moreover, WRF physical configuration ensemble increased percentage of skillful probabilistic forecasts from CFS initial condition ensemble.

Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts

Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts

Seasonal prediction of winter extreme precipitation over Canada by support vector regression

Abstract. For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter maximum 5-d accumulated precipitation anomalies were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern). The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN), and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Prairies and the weakest nonlinearity over the Arctic.

Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts

ENSEMBLES stream 2 single-model and multimodel ensemble seasonal precipitation forecasts are evaluated over the African continent with respect to Global Precipitation Climatology Centre (GPCC) precipitation data for the 1960–2005 time period using deterministic and probabilistic skill scores. Focus is set on three regions of Africa during main precipitation seasons: West Africa during boreal summer, southern Africa during austral summer and the Greater Horn of Africa during the ‘long rains’ and ‘short rains’ transition seasons. The 45-member multimodel improves the ensemble spread-skill ratio over all regions, which translates into enhanced skill in terms of anomaly correlation and ranked probability skill scores for climatological precipitation deciles overWest Africa and southern Africa. Results are contrasted depending on the region and probabilistic formulations of the ensemble predictions after a quantile—quantile calibration give valuable information essentially over areas where deterministic skill is found. Probabilistic skill scores illustrate the range of possibilities formore user-related applications of ensemble seasonal forecasts.Asimple illustration using a cost-loss model shows that model potential economic values can reach over 10% depending on the regions and occurrences studied.

SST clustering for winter precipitation prediction in southeast of Iran: Comparison between modified K-means and genetic algorithm-based clustering methods

In this paper, two innovative approaches for temporal clustering of sea surface temperature (SST) data using genetic algorithm (GA) and K-means clustering methods are introduced. In both methods, new approaches have been developed to consider the relationship between variations of the variable being clustered (SST in this study) and another climatic variable (precipitation in this study) in selection of clusters. In the case study, these models are used for clustering SST in selected geographical zones in Gulf of Oman, Arabian Sea, and the northern part of Indian Ocean with respect to the precipitation variations in the selected rain gauges in Sistan and Baluchestan Province in southeast of Iran. For this purpose, fitness function of the GA model and Euclidean distance in the modified K-means method have been formulated to minimize the variance of the precipitation data associated with each selected cluster. Application of these modified clustering methods in the case study has resulted in temporal classification of SST data which also represent below and above normal precipitation seasons in the selected rain gauges. The results of the two clustering techniques have been used for development of seasonal precipitation prediction guidelines based on the SST variations in the selected geographical zones. The results have also shown that these models can be effectively used for prediction of below and above normal precipitation seasons in the study area. Comparison between the results of this study with the official forecasts of the Islamic Republic of Iran Meteorological Organization (IRIMET) has shown significant improvements.

Deficiencies and possibilities for long-lead coupled climate prediction of the Western North Pacific-East Asian summer monsoon

Long-lead prediction of waxing and waning of the Western North Pacific (WNP)-East Asian (EA) summer monsoon (WNP-EASM) precipitation is a major challenge in seasonal time-scale climate prediction. In this study, deficiencies and potential for predicting the WNP-EASM precipitation and circulation one or two seasons ahead were examined using retrospective forecast data for the 26-year period of 1981–2006 from two operational couple models which are the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the Bureau of Meteorology Research Center (BMRC) Predictive Ocean–Atmosphere Model for Australia (POAMA). While both coupled models have difficulty in predicting summer mean precipitation anomalies over the region of interest, even for a 0-month lead forecast, they are capable of predicting zonal wind anomalies at 850 hPa several months ahead and, consequently, satisfactorily predict summer monsoon circulation indices for the EA region (EASMI) and for the WNP region (WNPSMI). It should be noted that the two models’ multi-model ensemble (MME) reaches 0.40 of the correlation skill for the EASMI with a January initial condition and 0.75 for the WNPSMI with a February initial condition. Further analysis indicates that prediction reliability of the EASMI is related not only to the preceding El Nino and Southern Oscillation (ENSO) but also to simultaneous local SST variability. On other hand, better prediction of the WNPSMI is accompanied by a more realistic simulation of lead–lag relationship between the index and ENSO. It should also be noted that current coupled models have difficulty in capturing the interannual variability component of the WNP-EASM system which is not correlated with typical ENSO variability. To improve the long-lead seasonal prediction of the WNP-EASM precipitation, a statistical postprocessing was developed based on the multiple linear regression method. The method utilizes the MME prediction of the EASMI and WNPSMI as predictors. It is shown that the statistical postprocessing is able to improve forecast skill for the summer mean precipitation over most of the WNP-EASM region at all forecast leads. It is noteworthy that the MME prediction, after applying statistical postprocessing, shows the best anomaly pattern correlation skill for the EASM precipitation at a 4-month lead (February initial condition) and for the WNPSM precipitation at a 5-month lead (January initial condition), indicating its potential for improving long-lead prediction of the monsoon precipitation.

Predictability of Seasonal Precipitation Using Joint Probabilities

Abstract This paper tests whether seasonal mean precipitation is predictable using a new method that estimates and analyzes joint probabilities. The new estimation method is to partition the globe into boxes, pool all data within the box to estimate a single joint probability of precipitation for two consecutive seasons, and then apply the resulting joint probability to individual pixels in the box. Pooling data in this way allows joint probabilities to be estimated in relatively small sample sizes; however, the new method assumes that the transition probabilities of pixels in a box are homogeneous and stationary. Joint probabilities are estimated from the Global Precipitation Climatology Project dataset in 21 land boxes and 5 ocean boxes during the period 1979–2008. The state of precipitation is specified by dry, wet, or normal terciles of the local climatological distribution. Predictability is quantified by mutual information, which is a fundamental measure of predictability that allows for nonlinear dependencies, and is tested using bootstrap methods. Predictability was verified by constructing probabilistic and quantitative forecasts directly from the transition probabilities and showing that they have superior cross-validated skills than forecasts based on climatology, persistence, or random selection. Spring was found to be the most predictable season, whereas summer was the least predictable season. Analysis of joint probabilities reveals that although the probabilities are close to climatology, the predictability of precipitation arises from a slight tendency of the state to persist from one season to the next, or if a transition occurs, then it is more often from one extreme to normal than from one extreme to the other.

Seasonal Ensemble Predictions of West African Monsoon Precipitation in the ECMWF System 3 with a Focus on the AMMA Special Observing Period in 2006

Abstract The West Africa monsoon precipitation of the ECMWF operational Seasonal Forecast System (SYS3) is evaluated at a lead time of 2–4 months in a 49-yr hindcast dataset, with special attention paid to the African Monsoon Multidisciplinary Analysis (AMMA) special observation period during 2006. In both the climatology and the year 2006 the SYS3 reproduces the progression of the West Africa monsoon but with a number of differences, most notably a southerly shift of the precipitation in the main monsoon months of July and August and the lack of preonset rainfall suppression and sudden onset jump. The model skill at predicting summer monsoon rainfall anomalies has increased in recent years indicating improvements in the ocean analysis since the 1990s. Examination of other model fields shows a widespread warm sea surface temperature (SST) bias exceeding 1.5 K in the Gulf of Guinea throughout the monsoon months in addition to a cold bias in the North Atlantic, which would both tend to enhance rainfall over the Gulf of Guinea coast at the expense of the monsoon rainfall over the Sahel. Seasonal forecasts were repeated for 2006 using the same release of the atmospheric forecast model forced by observed SSTs, and the monsoon rainfall reverts to its observed position, indicating the importance of the SST biases. A lack of stratocumulus off the west coast of Africa in SYS3 was hypothesized as a possible cause of the systematic rain and SST biases. Two more sets of ensembles were thus conducted with atmospheric model upgrades designed to tackle radiation, deep convection, and turbulence deficiencies. While these enhancements improve the simulation of stratocumulus significantly, it is found that the improvement in the warm SST bias is limited in scope to the southern cold tongue region. In contrast, the changes to the representation of convection cause an increase in surface downwelling shortwave radiation that, combined with latent heat flux changes associated with the wind stress field, increases the SST warm bias on and to the north of the equator. Thus, while the precipitation shortfall in the Sahel is reduced with the new physics, the overestimated rainfall of SYS3 in the coastal region is further enhanced, degrading the model systematic errors overall in the West Africa region. Finally, the difference in the systematic biases between the coupled and uncoupled systems was noted to be an impediment to the development of seamless forecasting systems.

Seasonal prediction of monthly precipitation in china using large-scale climate indices

In this study, seasonal predictions were applied to precipitation in China on a monthly basis based on a multivariate linear regression with an adaptive choice of predictors drawn from regularly updated climate indices with a two to twelve month lead time. A leave-one-out cross validation was applied to obtain hindcast skill at a 1% significance level. The skill of forecast models at a monthly scale and their significance levels were evaluated using Anomaly Correlation Coefficients (ACC) and Coefficients Of Determination (COD). The monthly ACC skill ranged between 0.43 and 0.50 in Central China, 0.41–0.57 in East China, and 0.41–0.60 in South China. The dynamic link between large-scale climate indices with lead time and the precipitation in China is also discussed based on Singular Value Decomposition Analysis (SVDA) and Correlation Analysis (CA).

Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States

Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region.

A New Way to Improve Seasonal Prediction by Diagnosing and Correcting the Intermodel Systematic Errors

Abstract Seasonal climate prediction, in general, can achieve excellent results with a multimodel system. A relevant calibration of individual models and an optimal combination of individual models are the key elements leading to this success. However, this commonly used approach appears to be insufficient to remove the intermodel systematic errors (IMSE), which represent similar error properties in individual models after their calibration. A new postprocessing method is proposed to correct the IMSE and to increase the prediction skill. The first step consists of carrying out a diagnosis on the calibrated errors before constructing the multimodel ensemble. In contrast to previous studies, the calibrated errors here are treated directly as the investigation target, and temporal correlation coefficients between the calibrated errors and other meteorological variables are calculated. In the second stage, mathematical and statistical tools are applied in an effort to forecast the IMSE in individual models. Then, the IMSE are removed from the calibrated results and the new corrected data are used to construct the multimodel ensemble. The hindcast of the European Union–funded Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel system is used to test the method. The simulated Southern Oscillation index is used to diagnose and to correct the calibrated errors of the simulated precipitation. The prediction qualities of the corrected data are assessed and compared with those of the uncorrected dataset. The results show that it is feasible to improve seasonal precipitation prediction by forecasting and correcting the IMSE. This improvement is visible not only for the individual models, but also for the multimodel ensemble.

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.

Potential Predictability of Long-Term Drought and Pluvial Conditions in the U.S. Great Plains

Abstract This study examines the predictability of seasonal mean Great Plains precipitation using an ensemble of century-long atmospheric general circulation model (AGCM) simulations forced with observed sea surface temperatures (SSTs). The results show that the predictability (intraensemble spread) of the precipitation response to SST forcing varies on interannual and longer time scales. In particular, this study finds that pluvial conditions are more predictable (have less intraensemble spread) than drought conditions. This rather unexpected result is examined in the context of the physical mechanisms that impact precipitation in the Great Plains. These mechanisms include El Niño–Southern Oscillation’s impact on the planetary waves and hence the Pacific storm track (primarily during the cold season), the role of Atlantic SSTs in forcing changes in the Bermuda high and low-level moisture flux into the continent (primarily during the warm season), and soil moisture feedbacks (primarily during the warm season). It is found that the changes in predictability are primarily driven by changes in the strength of the land–atmosphere coupling, such that under dry conditions a given change in soil moisture produces a larger change in evaporation and hence precipitation than the same change in soil moisture would produce under wet soil conditions. The above changes in predictability are associated with a negatively skewed distribution in the seasonal mean precipitation during the warm season—a result that is not inconsistent with the observations.

Error analysis of dynamical seasonal predictions of summer precipitation over the East Asian‐western Pacific region

In the East Asian‐western Pacific (EAWP) region, six climate prediction models which are currently used in APEC Climate Center (APCC) Multi‐Model Ensemble (MME) prediction system show low skill in predicting summer precipitation. In order to diagnose the dominant error in the prediction, an error decomposition method is developed. Using this method, the total error is decomposed into three parts: errors due to conditional bias, unconditional bias and atmospheric internal processes (AIP). The bias‐error can be corrected while the AIP error cannot. It is found that the sum of both bias‐errors is about 3 to 20 times larger than the AIP error over the EAWP region for these models. This suggests that the AIP error is not the main cause for the loss of predictability, therefore there is potential to improve the predictions by correcting the bias‐errors.