def METHOD_NAME():
    """Create a graphic with the additive adjustment factors estimated after applying the adapt_freq method."""
    n = 10000
    x = series(synth_rainfall(2, 2, wet_freq=0.25, size=n), "pr")  # sim
    y = series(synth_rainfall(2, 2, wet_freq=0.5, size=n), "pr")  # ref
    xp = adapt_freq(x, y, thresh=0).sim_ad
    fig, (ax1, ax2) = plt.subplots(2, 1)
    sx = x.sortby(x)
    sy = y.sortby(y)
    sxp = xp.sortby(xp)
    # Original and corrected series
    ax1.plot(sx.values, color="blue", lw=1.5, label="x : sim")
    ax1.plot(sxp.values, color="pink", label="xp : sim corrected")
    ax1.plot(sy.values, color="k", label="y : ref")
    ax1.legend()
    # Compute qm factors
    qm_add = QuantileDeltaMapping(kind="+", group="time").train(y, x).ds
    qm_mul = QuantileDeltaMapping(kind="*", group="time").train(y, x).ds
    qm_add_p = QuantileDeltaMapping(kind="+", group="time").train(y, xp).ds
    qm_mul_p = QuantileDeltaMapping(kind="*", group="time").train(y, xp).ds
    qm_add.cf.plot(ax=ax2, color="cyan", ls="--", label="+: y-x")
    qm_add_p.cf.plot(ax=ax2, color="cyan", label="+: y-xp")
    qm_mul.cf.plot(ax=ax2, color="brown", ls="--", label="*: y/x")
    qm_mul_p.cf.plot(ax=ax2, color="brown", label="*: y/xp")
    ax2.legend(loc="upper left", frameon=False)
    return fig