A two-step approach to model precipitation extremes in California based on max-stable and marginal point processes

TL;DR

This paper introduces a two-step statistical method combining daily records and block maxima to improve modeling of precipitation extremes in California, enhancing efficiency and detection of climate effects.

Contribution

It presents a novel two-step approach that separates marginal and dependence parameter estimation, utilizing more data than traditional max-stable models.

Findings

More efficient than traditional methods in simulations

Detected more sites affected by El Niño with narrower confidence intervals

Provided tighter confidence regions for joint extreme event risks

Abstract

In modeling spatial extremes, the dependence structure is classically inferred by assuming that block maxima derive from max-stable processes. Weather stations provide daily records rather than just block maxima. The point process approach for univariate extreme value analysis, which uses more historical data and is preferred by some practitioners, does not adapt easily to the spatial setting. We propose a two-step approach with a composite likelihood that utilizes site-wise daily records in addition to block maxima. The procedure separates the estimation of marginal parameters and dependence parameters into two steps. The first step estimates the marginal parameters with an independence likelihood from the point process approach using daily records. Given the marginal parameter estimates, the second step estimates the dependence parameters with a pairwise likelihood using block maxima. In a simulation study, the two-step approach was found to be more efficient than the pairwise likelihood approach using only block maxima. The method was applied to study the effect of El Ni\~{n}o-Southern Oscillation on extreme precipitation in California with maximum daily winter precipitation from 35 sites over 55 years. Using site-specific generalized extreme value models, the two-step approach led to more sites detected with the El Ni\~{n}o effect, narrower confidence intervals for return levels and tighter confidence regions for risk measures of jointly defined events.