Seasonal Precipitation Prediction Research Articles

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.

On the interpretation of seasonal Southern Africa precipitation prediction skill estimates during Austral summer

Differences between two types of prediction skill estimates over Southern Africa are illustrated to better inform the users of seasonal precipitation forecasts over the region who desire assessments of forecast accuracy. Both seasonal precipitation prediction skill estimates for the African continent south of 15°S during the December–March rainy season are derived from the perfect-model method. The perfect-model method is based on a 40-member ensemble of Community Atmosphere Model version 5 simulations forced by observed time-evolving boundary conditions during 1920–2016. The first skill estimate is based on the verification of an ensemble mean forecast spanning many seasons and therefore unconditional on a single boundary forcing. The second skill estimate is based on the verification of an ensemble mean forecast for a single season and is therefore conditional on that year’s boundary forcing. Unconditional prediction skill calculated in 30-year increments for each of the 40 possible forecasts reveals: (1) large spread in skill among the individual forecasts for any given year and (2) temporal variations in skill for each forecast. The magnitude of conditional prediction skill varies greatly from 1 year to the next, revealing that the boundary conditions offer little prediction skill during some years and comparably large skill during others. The simultaneous behaviors of the El Nino–Southern Oscillation and the subtropical Indian Ocean Dipole are related to the largest conditional precipitation prediction skill years. Unconditional skill estimates may therefore mislead users of forecasts who desire assessments of forecast accuracy. Unconditional skill may be temporally unstable, and unlike conditional skill, is not representative of the skill for a given season.

A decision‐tree approach to seasonal prediction of extreme precipitation in eastern China

AbstractSeasonal prediction of extreme precipitation has long been a challenge especially for the East Asian Summer Monsoon region, where extreme rains are often disastrous for the human society and economy. This paper introduces a decision‐tree (DT) method for predicting extreme precipitation in the rainy season over South China in April–June (SC‐AMJ) and the North China Plain in July–August (NCP‐JA). A number of preceding climate indices are adopted as predictors. In both cases, the DT models involving ENSO and NAO indices exhibit the best performance with significant skills among those with other combinations of predictors and are superior to their linear counterpart, the binary logistic regression model. The physical mechanisms for the DT results are demonstrated by composite analyses of the same DT path samples. For SC‐AMJ, an extreme season can be determined mainly via two paths: the first follows a persistent negative NAO phase in February–March; the second goes with decaying El Niño. For NCP‐JA, an extreme season can also be traced via two paths: the first is featured by “non El Niño” and an extremely negative NAO phase in the preceding winter; the second follows a shift from El Niño in the preceding winter to La Niña in the early summer. Most of the mechanisms underlying the decision rules have been documented in previous studies, while some need further studies. The present results suggest that the decision‐tree approach takes advantage of discovering and incorporating various nonlinear relationships in the climate system, hence is of great potential for improving the prediction of seasonal extreme precipitation for given regions with increasing sample observations.

Monthly and Seasonal Prediction of Precipitation and Air Temperature in the Amur River Basin

The ability to forecast air temperature and precipitation at monthly-seasonal time scale in the Amur River basin is assessed. The climatic characteristics of actual monthly values of air temperature and total precipitation are compared with those based on the semi-Lagrangian global finite-difference model (SL-AV) of general atmospheric circulation for the Amur basin. It is shown that there is high correlation between total precipitation for 1–2 months and water discharge in the Amur River in next months during the warm half-year. The SL-AV model forecasts of air temperature and precipitation in the Amur basin are verified. It is demonstrated that the accuracy of prediction of total precipitation averaged over the Amur basin is higher as compared with that for initial gridded forecasts. The accuracy of such forecasts is rather high for July and August being critical months for the flood formation. The procedure for the statistical correction of monthly and seasonal forecasts of air temperature and precipitation based on the SL-AV model and the regression scheme for the Amur runoff prediction are proposed.

Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation

Abstract Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO–temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.

Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation

The seasonal prediction of sea surface temperature (SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model (FIO-ESM) is assessed in this study. The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions. The average skill of the North Pacific variability (NPV) index from 1 to 6 months lead is as high as 0.72 (0.55) when El Nino-Southern Oscillation and NPV are in phase (out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6% (23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.

A comparison of CCSM4 high-resolution and low-resolution predictions for south Florida and southeast United States drought

It is important to have confidence in seasonal climate predictions of precipitation, particularly related to drought, as implications can be far-reaching and costly—this is particularly true for Florida. Precipitation can vary on fine spatial resolutions, and high-resolution coupled models may be needed to correctly represent precipitation variability. We study south Florida and southeast United States drought in Community Climate System version 4 low resolution (1° atmosphere/ocean) and high-resolution (0.5°atmosphere/0.1°ocean) predictions for time means ranging from 3 to 36 months. The very high-resolution in the ocean is of interest here given the potential importance of Gulf Stream on south Florida rainfall. Skill of shorter time-mean South Florida predictions (i.e. 3- and 12-months) are not impacted by increased resolution, but skill of 36-month mean south Florida precipitation is somewhat increased in the high resolution predictions. Notably, over the broader southeast United States the high-resolution model has higher skill for the 36-month mean rainfall predictions, associated with an improved relationship with tropical Pacific and Gulf Stream SSTA. Why this improvement in the broader southeast United States does not extend to Florida is an open question, but does suggest that even further resolution refinements may be needed.

Predictability of seasonal precipitation across major crop growing areas in Colombia

Agriculture is one of the sectors that has greatly benefitted from the establishment of climate services. In Colombia, interannual climate variability can disrupt agricultural production, lower farmers' incomes and increase market prices. Increasing demand thus exists for agro-climatic services in the country. Fulfilling such demand requires robust and consistent approaches for seasonal climate forecasting. Here, we assess seasonal precipitation predictability and forecast skill at agriculturally-relevant timescales for five departments that represent key growing areas of major staple crops (rice, maize, and beans). Analyses use Canonical Correlation Analysis, with both observed SSTs and modeled (NCEP-CFSv2) SSTs, as well as with CFSv2 predicted precipitation fields (through a Model-Output-Statistics analysis). Some 74.4% of the forecast situations analyzed (5 departments ∗ 4 seasons ∗ 3 predictors ∗ 3 lead times) showed correlation-based goodness index (Kendall’s tau, τ-) values above 0.1, 38.8% above 0.2, and 18.8% above 0.3. Predictability was limited towards eastern Colombia, and during wet periods of the year in the Inter-Andean Valleys. Importantly, results were consistent between ERSST and CFSv2-driven forecasts, implying that both can offer valuable outlooks for Colombia. While our study is a first important step toward the establishment of a sustainable and successful climate service for agriculture in Colombia, further work is required to (1) improve seasonal forecast skill; (2) link seasonal forecasts to agricultural modelling applications; (3) design appropriate delivery means; and (4) establish stakeholder-driven processes that allow two-way communication between forecast issuing institutions (e.g. IDEAM–Colombian Meteorological Service) and famers’ organizations and farming communities.

Seasonal Predictability of Summer Rainfall over South America

The seasonal predictability of austral summer rainfall is evaluated in a set of retrospective forecasts (hindcasts) performed as part of the Minerva and Metis projects. Both projects use the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) coupled to the Nucleus for European Modelling of the Ocean (NEMO). The Minerva runs consist of three sets of hindcasts where the spatial resolution of the model’s atmospheric component is progressively increased while keeping the spatial resolution of its oceanic component constant. In the Metis runs, the spatial resolution of both the atmospheric and oceanic components are progressively increased. We find that raw model predictions show seasonal forecast skill for rainfall over northern and southeastern South America. However, predictability is difficult to detect on a local basis, but it can be detected on a large-scale pattern basis. In addition, increasing horizontal resolution does not lead to improvements in the forecast skill of rainfall over South America. A predictable component analysis shows that only the first predictable component of austral summer precipitation has forecast skill, and the source of forecast skill is El Niño–Southern Oscillation. Seasonal prediction of precipitation remains a challenge for state-of-the-art climate models. Positive benefits of increasing model resolution might be more evident in other atmospheric fields (i.e., temperature or geopotential height) and/or temporal scales (i.e., subseasonal temporal scales).

Snow–precipitation coupling and related atmospheric feedbacks over North America

Understanding snow–precipitation coupling mechanisms is of great importance both from theoretical and operational considerations. Here, carefully designed climate model experiments, with and without interactive snow, are conducted to study snow–precipitation coupling mechanisms over North America. Coupling hotspots are identified over southern Canada during December and over the central United States during January. The hotspot over southern Canada involves a positive snow–atmosphere feedback mechanism, whereby snow modifies the large‐scale atmospheric features, which resembles the positive phase of North Atlantic Oscillation. This favors storm activity and enhanced snow over the region. The coupling over the central United States during January, on the other hand, is tied to the albedo effect of snow, which leads to cooling of the lower atmosphere, which in turn determines the precipitation phase, favoring snow formation over rain. The results from this study, in general, are informative for sub‐seasonal to seasonal prediction of winter precipitation for the studied regions.

Seasonal precipitation forecast over Mexico based on a hybrid statistical–dynamical approach

We introduce a new hybrid scheme for seasonal precipitation prediction over the Mexican territory. The scheme takes as input sea surface temperature (SST) predicted from the ECMWF System 4 coupled model (Sys4) identifying three predictors, namely SST over the western Pacific (WP), eastern Pacific (EP) and tropical Atlantic (TA); these predictors are used to forecast precipitation on a high‐resolution grid via a multiple regression model. Comparison of the performance of the hybrid scheme with the Sys4 model for ensemble re‐forecasts for the period 1982–2010 shows that the hybrid model adds skill to the Sys4 forecast, with higher correlations and Heidke skill scores throughout most of the Mexican territory, both in winter and summer. In winter the contribution of the EP is dominant in modulating precipitation via the effects of El Niño‐Southern Oscillation (ENSO), while in summer the contributions of the WP and TA are dominant in different Mexican regions. The hybrid scheme, applied to high‐resolution precipitation observations, also produces significant fine‐scale variability of forecast skill and mechanism contributions. Overall, we show that our hybrid approach can provide a useful tool to improve seasonal precipitation forecasts over Mexico.

Evaluation of performance of seasonal precipitation prediction at regional scale over India

The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982–2008) as well as independent predictions for the period 2009–2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions as well as India as a whole during all the four seasons.

Monthly streamflow forecasting at varying spatial scales in the Rhine basin

Abstract. Model output statistics (MOS) methods can be used to empirically relate an environmental variable of interest to predictions from earth system models (ESMs). This variable often belongs to a spatial scale not resolved by the ESM. Here, using the linear model fitted by least squares, we regress monthly mean streamflow of the Rhine River at Lobith and Basel against seasonal predictions of precipitation, surface air temperature, and runoff from the European Centre for Medium-Range Weather Forecasts. To address potential effects of a scale mismatch between the ESM's horizontal grid resolution and the hydrological application, the MOS method is further tested with an experiment conducted at the subcatchment scale. This experiment applies the MOS method to 133 additional gauging stations located within the Rhine basin and combines the forecasts from the subcatchments to predict streamflow at Lobith and Basel. In doing so, the MOS method is tested for catchments areas covering 4 orders of magnitude. Using data from the period 1981–2011, the results show that skill, with respect to climatology, is restricted on average to the first month ahead. This result holds for both the predictor combination that mimics the initial conditions and the predictor combinations that additionally include the dynamical seasonal predictions. The latter, however, reduce the mean absolute error of the former in the range of 5 to 12 %, which is consistently reproduced at the subcatchment scale. An additional experiment conducted for 5-day mean streamflow indicates that the dynamical predictions help to reduce uncertainties up to about 20 days ahead, but it also reveals some shortcomings of the present MOS method.

Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Abstract. Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills.

Improved seasonal prediction of UK regional precipitation using atmospheric circulation

ABSTRACTThe aim of this study is to further our understanding of whether skilful seasonal forecasts of the large‐scale atmospheric circulation can be downscaled to provide skilful seasonal forecasts of regional precipitation. A simple multiple linear regression model is developed to describe winter precipitation variability in nine UK regions. The model for each region is a linear combination of two mean sea‐level pressure (MSLP)‐based indices which are derived from the MSLP correlation patterns for precipitation in northwest Scotland and southeast England. The first index is a pressure dipole, similar to the North Atlantic Oscillation but shifted to the east; the second index is the MSLP anomaly centred over the UK. The multiple linear regression model describes up to 76% of the observed precipitation variability in each region and gives higher correlations with precipitation than using either of the two indices alone. The Met Office's seasonal forecast system (GloSea5) is found to have significant skill in forecasting the two MSLP indices for the winter season, in forecasts initialized around the start of November. Applying the multiple linear regression model to the GloSea5 hindcasts is shown to give improved skill over the precipitation forecast by the GloSea5, with the largest improvement in Scotland.

The Madden-Julian Oscillation: A Tool for Regional Seasonal Precipitation Outlooks?

The Madden-Julian Oscillation (MJO) is an important intraseasonal climate signal which circles the global tropics, but also impacts extratropical weather regimes. Few studies have investigated whether the MJO is a source of regional seasonal climate predictability. The present objective is to determine the extent to which the season and phase (geographic location) of MJO contribute to the frequency of global rainfall anomalies in ensuing seasons. Indices of June-July-August and December-January-February MJO activity for each phase and the El Niño/Southern Oscillation (ENSO) were correlated to three-month averages of rainfall up to a six-month lead time. Field significance was calculated and patterns of the relationships were described. In general, MJO shows some skill in regional seasonal precipitation prediction, but to a lesser extent than ENSO. However, the presence of MJO in the western Indian Ocean and near the date line did reveal a persistent and significant relationship with regional seasonal rainfall, especially over Northern Hemisphere land areas.

Skillful seasonal predictions of winter precipitation over southern China

Southern China experiences large year-to-year variability in the amount of winter precipitation, which can result in severe social and economic impacts. In this study, we demonstrate prediction skill of southern China winter precipitation by three operational seasonal prediction models: the operational Global seasonal forecasting system version 5 (GloSea5), the NCEP Climate Forecast System (CFSv2) and the Beijing Climate Center Climate System Model (BCC-CSM1.1m). The correlation scores reach 0.76 and 0.67 in GloSea5 and CFSv2, respectively; and the amplitude of the ensemble mean forecast signal is comparable to the observed variations. The skilful predictions in GloSea5 and CFSv2 mainly benefit from the successful representation of the observed ENSO teleconnection. El Niño weakens the Walker circulation and leads to the strengthening of the subtropical high over the northwestern Pacific. The anti-cyclone then induces anomalous northward flow over the South China Sea and brings water vapor to southern China, resulting in more precipitation. This teleconnection pattern is too weak in BCC-CSM1.1m, which explains its low skill (0.13). Whereas the most skilful forecast system is also able to simulate the influence of the Indian Ocean on southern China precipitation via changes in southwesterly winds over the Bay of Bengal. Finally, we examine the real-time forecast for 2015/16 winter when a strong El Niño event led to the highest rainfall over southern China in recent decades. We find that the GloSea5 system gave good advice as it produced the third wettest southern China in the hindcast, but underestimated the observed amplitude. This is likely due to the underestimation of the Siberian High strength in 2015/2016 winter, which has driven strong convergence over southern China. We conclude that some current seasonal forecast systems can give useful warning of impending extremes. However, there is still need for further model improvement to fully represent the complex dynamics of the region.

The role of the subtropical North Atlantic water cycle in recent US extreme precipitation events

The role of the oceanic water cycle in the record-breaking 2015 warm-season precipitation in the US is analyzed. The extreme precipitation started in the Southern US in the spring and propagated northward to the Midwest and the Great Lakes in the summer of 2015. This seasonal evolution of precipitation anomalies represents a typical mode of variability of US warm-season precipitation. Analysis of the atmospheric moisture flux suggests that such a rainfall mode is associated with moisture export from the subtropical North Atlantic. In the spring, excessive precipitation in the Southern US is attributable to increased moisture flux from the northwestern portion of the subtropical North Atlantic. The North Atlantic moisture flux interacts with local soil moisture which enables the US Midwest to draw more moisture from the Gulf of Mexico in the summer. Further analysis shows that the relationship between the rainfall mode and the North Atlantic water cycle has become more significant in recent decades, indicating an increased likelihood of extremes like the 2015 case. Indeed, two record-high warm-season precipitation events, the 1993 and 2008 cases, both occurred in the more recent decades of the 66 year analysis period. The export of water from the North Atlantic leaves a marked surface salinity signature. The salinity signature appeared in the spring preceding all three extreme precipitation events analyzed in this study, i.e. a saltier-than-normal subtropical North Atlantic in spring followed by extreme Midwest precipitation in summer. Compared to the various sea surface temperature anomaly patterns among the 1993, 2008, and 2015 cases, the spatial distribution of salinity anomalies was much more consistent during these extreme flood years. Thus, our study suggests that preseason salinity patterns can be used for improved seasonal prediction of extreme precipitation in the Midwest.

Seasonal precipitation prediction via data‐adaptive principal component regression

ABSTRACTThis article studies a problem of predicting seasonal precipitation over East Asia from real observations and multi‐model ensembles. Classical model output statistics approach based on principal component analysis (PCA) has been widely used for climate prediction. However, it may not be efficient in predicting precipitation since PCA assumes that information of data should be retained by the second moment of them, which is too stringent to climate data that can be skewed or asymmetric. This article presents a method based on data‐adaptive PCA (DPCA) by Lim and Oh (2016) that can adapt to non‐Gaussian distributed data. In addition to investigate the utility of DPCA for climate study, we propose a data‐adaptive principal component regression for seasonal precipitation prediction, which consists of DPCA and a regularized regression technique that is able to handle high‐dimensional data. We apply the proposed method to nine general circulation models for prediction of precipitations on the summer season (June, July, and August). The prediction ability of the proposed method is evaluated in comparison with observations and model outputs (prediction) of each constituent model.

Optimal Physics Parameterization Scheme Combination of the Weather Research and Forecasting Model for Seasonal Precipitation Simulation over Ghana

Seasonal predictions of precipitation, among others, are important to help mitigate the effects of drought and floods on agriculture, hydropower generation, disasters, and many more. This work seeks to obtain a suitable combination of physics schemes of the Weather Research and Forecasting (WRF) model for seasonal precipitation simulation over Ghana. Using the ERA-Interim reanalysis as forcing data, simulation experiments spanning eight months (from April to November) were performed for two different years: a dry year (2001) and a wet year (2008). A double nested approach was used with the outer domain at 50 km resolution covering West Africa and the inner domain covering Ghana at 10 km resolution. The results suggest that the WRF model generally overestimated the observed precipitation by a mean value between 3% and 64% for both years. Most of the scheme combinations overestimated (underestimated) precipitation over coastal (northern) zones of Ghana for both years but estimated precipitation reasonably well over forest and transitional zones. On the whole, the combination of WRF Single-Moment 6-Class Microphysics Scheme, Grell-Devenyi Ensemble Cumulus Scheme, and Asymmetric Convective Model Planetary Boundary Layer Scheme simulated the best temporal pattern and temporal variability with the least relative bias for both years and therefore is recommended for Ghana.