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<p>Using the method of offline radiative transfer modeling within the partial radiative perturbation (PRP) approach, the effective radiative forcing by aerosol–cloud interactions (<span class="inline-formula">ERF_aci</span>) in the ECHAM–HAMMOZ aerosol climate model is decomposed into a radiative forcing by anthropogenic cloud droplet number change and adjustments of the liquid water path and cloud fraction. The simulated radiative forcing by anthropogenic cloud droplet number change and liquid water path adjustment are of approximately equal magnitude at <span class="inline-formula">−0.52</span> and <span class="inline-formula">−0.53</span>&thinsp;<span class="inline-formula">W m⁻²</span>, respectively, while the cloud-fraction adjustment is somewhat weaker at <span class="inline-formula">−0.31</span>&thinsp;<span class="inline-formula">W m⁻²</span> (constituting 38&thinsp;%, 39&thinsp;%, and 23&thinsp;% of the total <span class="inline-formula">ERF_aci</span>, respectively); geographically, all three <span class="inline-formula">ERF_aci</span> components in the simulation peak over China, the subtropical eastern ocean boundaries, the northern Atlantic and Pacific oceans, Europe, and eastern North America (in order of prominence). Spatial correlations indicate that the temporal-mean liquid water path adjustment is proportional to the temporal-mean radiative forcing, while the relationship between cloud-fraction adjustment and radiative forcing is less direct. While the estimate of warm-cloud <span class="inline-formula">ERF_aci</span> is relatively insensitive to the treatment of ice and mixed-phase cloud overlying warm cloud, there are indications that more restrictive treatments of ice in the column result in a low bias in the estimated magnitude of the liquid water path adjustment and a high bias in the estimated magnitude of the droplet number forcing. Since the present work is the first PRP decomposition of the aerosol effective radiative forcing into radiative forcing and rapid cloud adjustments, idealized experiments are conducted to provide evidence that the PRP results are accurate. The experiments show that using low-frequency (daily or monthly) time-averaged model output of the cloud property fields underestimates the ERF, but 3-hourly mean output is sufficiently frequent.</p>