Contact Dr. Alex J. Cannon:

Phone & Address



Adjunct professor, Atmospheric Science, University of British Columbia (UBC)


Ph.D. Atmospheric Science, UBC

M.Sc. Climatology, Dip. Meteorology, B.Sc. Physical Geography

WMO Research Award for Young Scientists

CMOS Tertia M.C. Hughes Award

Environment Canada Citation of Excellence


Dr. Alex J. Cannon

Research Scientist - Research on climate extremes and climate projections.

CURRENT S&T / RESEARCH - Activities that contribute to the understanding of the state, trends, variability, extremes, and future projections of climate at both global and regional scales.


Editor-in-Chief (Meteorological and Hydrological Sciences), Atmosphere-Ocean

Associate Editor, Stochastic Environmental Research and Risk Assessment

Editorial Advisory Board, Computers & Geosciences

Committee on Artificial Intelligence Applications to Environmental Science, American Meteorological Society (2011-2017)


Cannon, A.J., 2017. Multivariate quantile mapping bias correction: An N-dimensional probability density function transform for climate model simulations of multiple variables. Climate Dynamics, doi:10.1007/s00382-017-3580-6

Zhang, X., F.W. Zwiers, G. Li, X. Wan, and A.J. Cannon, 2017. Complexity in estimating past and future extreme short-duration rainfall. Nature Geoscience, doi:10.1038/NGEO2911

Mahony, C., A.J. Cannon, T. Wang, and S. Aitken, 2017. A closer look at novel climates: new method and insights at continental to landscape scales. Global Change Biology, doi:10.1111/gcb.13645

Cannon, A.J., 2016. Multivariate bias correction of climate model outputs: matching marginal distributions and inter-variable dependence structure. Journal of Climate, 29(19):7045–7064. doi:10.1175/JCLI-D-15-0679.1

Werner, A.T. and A.J. Cannon, 2016. Hydrologic extremes – An intercomparison of multiple gridded statistical downscaling methods. Hydrology and Earth System Sciences, 20: 1483-1508. doi:10.5194/hess-20-1483-2016

Cannon, A.J., 2015. Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. Journal of Climate, 28(3): 1260-1267. doi:10.1175/JCLI-D-14-00636.1

Cannon, A.J., S.R. Sobie, and T.Q. Murdock, 2015. Bias correction of simulated precipitation by quantile mapping: how well do methods preserve relative changes in quantiles and extremes? Journal of Climate, 28(17): 6938-6959. doi:10.1175/JCLI-D-14-00754.1

Cannon, A.J., 2015. Revisiting the nonlinear relationship between ENSO and winter extreme station precipitation in North America. International Journal of Climatology, 35: 4001-4014. doi:10.1002/joc.4263

Radic, V., A.J. Cannon, B. Menounos, and C. Gi, 2015. Future changes in autumn atmospheric river events in British Columbia, Canada, as projected by CMIP5 global climate models. Journal of Geophysical Research: Atmospheres, 120(18): 9279-9302. doi:10.1002/2015JD023279

Schnorbus, M.A. and A.J. Cannon, 2014. Statistical emulation of streamflow projections from a distributed hydrological model: application to CMIP3 and CMIP5 climate projections for British Columbia, Canada. Water Resources Research, 50(11): 8907-8926. doi:10.1002/2014WR015279

Bürger, G., S.R. Sobie, A.J. Cannon, A.T. Werner, and T.Q. Murdock, 2013. Downscaling extremes - an intercomparison of multiple methods for future climate. Journal of Climate, 26: 3429-3449. doi:10.1175/JCLI-D-12-00249.1

Bürger, G., T.Q. Murdock, A.T. Werner, S.R. Sobie, and A.J. Cannon, 2012. Downscaling extremes - an intercomparison of multiple statistical methods for present climate. Journal of Climate, 25: 4366–4388. doi:10.1175/JCLI-D-11-00408.1

Cannon, A.J., 2011. Quantile regression neural networks: implementation in R and application to precipitation downscaling. Computers & Geosciences, 37: 1277-1284, doi:10.1016/j.cageo.2010.07.005

Cannon, A.J., 2010. A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology. Hydrological Processes, 24: 673-685.

Cannon, A.J., 2008. Probabilistic multi-site precipitation downscaling by an expanded Bernoulli-gamma density network. Journal of Hydrometeorology, 9(6):1284-1300.

Cannon, A.J. and W.W. Hsieh, 2008. Robust nonlinear canonical correlation analysis: Application to seasonal climate forecasting. Nonlinear Processes in Geophysics, 15: 221-232.

Stahl, K., R.D. Moore, J.M. Shea, D. Hutchinson, and A.J. Cannon, 2008. Coupled modelling of glacier and streamflow response to future climate scenarios. Water Resources Research, 44: W02422, doi:10.1029/2007WR005956

Scibek, J., D.M. Allen, A.J. Cannon, and P.H. Whitfield, 2007. Groundwater-surface water interaction under scenarios of climate change using a high-resolution transient groundwater model. Journal of Hydrology, 333: 165-181.

Cannon, A.J. and P.H. Whitfield, 2002. Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. Journal of Hydrology, 259: 136-151.

Cannon, A.J., P.H. Whitfield, and E.R. Lord, 2002. Synoptic map-pattern classification using recursive partitioning and principal component analysis. Monthly Weather Review, 130(5): 1187-1206.

Whitfield, P.H. and A.J. Cannon, 2000. Recent variations in climate and hydrology in Canada. Canadian Water Resources Journal, 25(1): 19-65.

Expertise Categories associated with this S&T Expert:

     Adaptation & Impacts
          Computer analysis and modelling
          Methods development
     Climate Change and Processes
          Adaptation and impacts
          Trends and variability
          Artificial intelligence
          Environmental prediction
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