Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling
15 July 2020

Seasonal Representation of Extreme Precipitation Indices Over the United States in CMIP6 Present-Day Simulations

While common biases persist in CMIP6 models, the multi-model mean out performs most individual models.


Our study provides a comprehensive assessment of how well modern (i.e., CMIP6) Earth system models represent extreme precipitation over the United States. Overall, the study findings identify common biases (e.g., overly frequent light rainfall, exaggerated winter amount/intensity in Western US, etc.) across CMIP6 models; show that the representation of many precipitation statistics in CMIP6 models is better in the summer than winter; and demonstrate that the CMIP6 multi‐model mean tends to be more reliable than any individual model for most extreme precipitation indices in comparison to gauge-based observations.


Extreme precipitation events can have large impacts on society and Earth system models can be useful tools for understanding the variability and changes in these events. Our study highlights the seasons and regions where CMIP6 models perform well and where they do not, which is critical for assessing confidence in their simulations.


Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States with a comprehensive set of extreme precipitation indices. The observations exhibit significant differences in their estimates of area-average frequency and amount distributions and spatial patterns of the mean and extremes precipitation over the United States. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly during summer in comparison to gauge-based data. Although the “standard” horizontal-resolution varies across CMIP6 models, from ~0.7˚ to ~2.8˚, we find that resolution is not a good indicator of model performance.

Akintomide Afolayan Akinsanola
University of Georgia
Akinsanola, AA, GJ Kooperman, AG Pendergrass, WM Hannah, and KA Reed.  2020.  "Seasonal Representation of Extreme Precipitation Indices Over the United States in CMIP6 Present-Day Simulations."  Environmental Research Letters.