Articles | Volume 12, issue 1
https://doi.org/10.5194/ascmo-12-149-2026
https://doi.org/10.5194/ascmo-12-149-2026
05 May 2026
 | 05 May 2026

Improving multisite precipitation generators based on generalised linear models

Jakob Benjamin Wessel and Richard E. Chandler

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Cited articles

Ailliot, P., Thompson, C., and Thomson, P.: Space-time modelling of precipitation using a hidden Markov model and censored Gaussian distributions, Appl. Statist., 58, 405–426, 2009. a
Ambrosino, C., Chandler, R. E., and Todd, M. C.: Southern African monthly rainfall variability: An analysis based on generalized linear models, J. Climate, 24, https://doi.org/10.1175/2010JCLI3924.1, 2011. a
Ambrosino, C., Chandler, R. E., and Todd, M. C.: Rainfall-derived growing season characteristics for agricultural impact assessments in South Africa, Theor. Appl. Climatol., 115, 411–426, https://doi.org/10.1007/s00704-013-0896-y, 2014. a, b, c, d, e
Andrianakis, I. and Challenor, P. G.: The effect of the nugget on Gaussian process emulators of computer models, Comput. Stat. Data An., 56, 4215–4228, https://doi.org/10.1016/j.csda.2012.04.020, 2012. a
Asong, Z. E., Khaliq, M. N., and Wheater, H. S.: Multisite multivariate modeling of daily precipitation and temperature in the Canadian Prairie Provinces using generalized linear models, Clim. Dynam., 47, 2901–2921, https://doi.org/10.1007/s00382-016-3004-z, 2016. a
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Short summary
Precipitation generators are statistical models for generating long synthetic sequences of (multisite) precipitation for hydrological analyses. One widely-used class of precipitation generators is based on so-called 'generalised linear models'.  In this work, we extend this class to better capture key features of daily precipitation and introduce a new method to ensure realistic inter-site dependence, so neighbouring locations tend to be dry or wet at the same time.
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