Spatio‐temporal precipitation climatology over complex terrain using a censored additive regression model

Abstract

Flexible spatio‐temporal models are widely used to create reliable and accurate estimates for precipitation climatologies. Most models are based on square root transformed monthly or annual means, where a normal distribution seems to be appropriate. This assumption becomes invalid on a daily time scale as the observations involve large fractions of zero observations and are limited to non‐negative values. We develop a novel spatio‐temporal model to estimate the full climatological distribution of precipitation on a daily time scale over complex terrain using a left‐censored normal distribution. The results demonstrate that the new method is able to account for the non‐normal distribution and the large fraction of zero observations. The new climatology provides the full climatological distribution on a very high spatial and temporal resolution, and is competitive with, or even outperforms existing methods, even for arbitrary locations.

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