Statistical extrapolation is required to predict extreme loads, associated with a target return period, for offshore wind turbines. In statistical extrapolation, "short-term" distributions of the load random variable(s) conditional on the environment are integrated with the joint probability distribution of environmental random variables (from wind, waves, current etc.) to obtain the so-called "long-term" distribution, from which long-term loads may be obtained for any return period. The accurate prediction of long-term extreme loads for offshore wind turbines, using efficient extrapolation procedures, is our main goal. While loads data, needed for extrapolation, are obtained by simulations in a design scenario, field data can be valuable for understanding the offshore environment and the resulting turbine response. We use limited field data from a 2MW turbine at the Blyth site in the United Kingdom, and study the influence of contrasting environmental (wind) regimes and associated waves at this site on long-term loads, derived using extrapolation. This study also highlights the need for efficient extrapolation procedures and for modeling nonlinear waves at sites with shallow water depths. An important first step in extrapolation is to establish robust short-term distributions of load extremes. Using data from simulations of a 5MW onshore turbine model, we compare empirical short-term load distributions when two alternative models for extremes--global and block maxima--are used. We develop a convergence criterion, based on controlling the uncertainty in rare load fractiles, which serves to assess whether or not an adequate number of simulations has been performed. To establish long-term loads for a 5MW offshore wind turbine, we employ an inverse reliability approach, which is shown to predict reasonably accurate long-term loads, compared to a more expensive direct integration approach. We show that blade pitching control actions can be a major source of response variability, due to which a large number of simulations may be required to obtain stable tails of short-term load distributions, and to predict accurate ultimate loads. We address model uncertainty as it pertains to wave models. We investigate the effect of using irregular nonlinear (second-order) waves, compared to irregular linear waves, on loads for an offshore wind turbine. We incorporate this nonlinear irregular wave model into a procedure for integrated wind-wave-response analysis of offshore wind turbines. We show that computed loads are generally somewhat larger with nonlinear waves and, hence, that modeling nonlinear waves is important is response simulations of offshore wind turbines and prediction of long-term loads.