  Most solar flares originate in sunspot groups, where magnetic field changes
lead to energy build-up and release. However, few flare-forecasting methods use
information of sunspot-group evolution, instead focusing on static
point-in-time observations. Here, a new forecast method is presented based upon
the 24-hr evolution in McIntosh classification of sunspot groups.
Evolution-dependent $\geqslant$C1.0 and $\geqslant$M1.0 flaring rates are found
from NOAA-numbered sunspot groups over December 1988 to June 1996 (Solar Cycle
22; SC22) before converting to probabilities assuming Poisson statistics. These
flaring probabilities are used to generate operational forecasts for sunspot
groups over July 1996 to December 2008 (SC23), with performance studied by
verification metrics. Major findings are: i) considering Brier skill score
(BSS) for $\geqslant$C1.0 flares, the evolution-dependent McIntosh-Poisson
method ($\text{BSS}_{\text{evolution}}=0.09$) performs better than the static
McIntosh-Poisson method ($\text{BSS}_{\text{static}} = -0.09$); ii) low BSS
values arise partly from both methods over-forecasting SC23 flares from the
SC22 rates, symptomatic of $\geqslant$C1.0 rates in SC23 being on average
$\approx$80% of those in SC22 (with $\geqslant$M1.0 being $\approx$50%); iii)
applying a bias-correction factor to reduce the SC22 rates used in forecasting
SC23 flares yields modest improvement in skill relative to climatology for both
methods ($\mathrm{BSS}^{\mathrm{corr}}_{\mathrm{static}} = 0.09$ and
$\mathrm{BSS}^{\mathrm{corr}}_{\mathrm{evolution}} = 0.20$) and improved
forecast reliability diagrams.
