## 1. Introduction

[2] A traditional assumption underlying flood frequency analysis is that the underlying stochastic process is stationary in time, and that the annual maximum flood corresponds to an independent identically distributed (iid) process. However, it is now widely acknowledged that both climate and land use changes modify flood frequency. *Hirschboeck* [1987a, 1987b, 1988] recognized that annual maximum floods at a given site could be due to markedly different rainfall or climate mechanisms that occur in different seasons, and explored the use of mixture models for estimating flood frequency. Changes in flood frequency over paleotimescales [*Porparto and Ridolfi*, 1998; *Knox*, 1993] have also been reported. The recognition that there is interannual to decadal organization in climate, as well as systematic, anthropogenic climate changes that affect flood mechanisms and hence lead to structured temporal variations in flood frequency is more recent [*Katz and Brown*, 1992; *Jain and Lall*, 2000; *Pizaro and Lall*, 2002; *Milly et al.*, 2002; *Franks and Kuczera*, 2002]. The management dilemma posed by the apparent increase in the flood threat to Sacramento, California, from the American River [*National Research Council* (*NRC*), 1995, 1999] is an example of such issues.

[3] This paper focuses on the treatment of changing flood frequency at a given site, where the nonstationarity is derived primarily from structured low-frequency climate variations, and surrogate records of climate indices that represent the essential modes of the underlying climate variations are available. Given these indices, one can (1) consider a diagnostic analysis (as in the work of *Jain and Lall* [2000, 2001]) that relates the flood series to appropriate climate indices; (2) carryout a prognostic analysis that uses climate indices to forecast season ahead (or longer) flood risk; (3) reconstruct flood quantiles over periods prior to the period covered by the historical flood record; and (4) improve regional flood frequency curves by recognizing that the nonoverlapping periods of record across sites may reflect different, yet identifiable climate conditions. Here, the second and third of these analyses are considered in the framework of a regression approach for estimating conditional flood quantiles. The focus is on documenting the relative merits of two methods for conditional flood quantile estimation as a building block toward these two objectives. The flood variable in such a setting may be the peak flow rate over the period of interest, or the n-day (e.g., 3 day) flood volume.

[4] A brief overview of the interconnections between low-frequency oscillations in the climate signals and flood variability is provided in the next section. The conditional flood quantile estimation problem is then introduced in this context and selected methodologies for estimation are reviewed in the third section. A Monte Carlo investigation with synthetic data used to compare two of these methods follows. An application to data from a basin in Montana is then presented.