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<p>We present an empirical model for nitric oxide (<span class="inline-formula">NO</span>) in the mesosphere (<span class="inline-formula">≈60</span>–90&thinsp;km) derived from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartoghraphY) limb scan data. This work complements and extends the NOEM (Nitric Oxide Empirical Model; <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx45">Marsh et al.</a>, <a href="#bib1.bibx45">2004</a></span>) and SANOMA (SMR Acquired Nitric Oxide Model Atmosphere; <span class="cit" id="xref_altparen.2"><a href="#bib1.bibx40">Kiviranta et al.</a>, <a href="#bib1.bibx40">2018</a></span>) empirical models in the lower thermosphere. The regression ansatz builds on the heritage of studies by <span class="cit" id="xref_text.3"><a href="#bib1.bibx37">Hendrickx et al.</a> (<a href="#bib1.bibx37">2017</a>)</span> and the superposed epoch analysis by <span class="cit" id="xref_text.4"><a href="#bib1.bibx60">Sinnhuber et al.</a> (<a href="#bib1.bibx60">2016</a>)</span> which estimate <span class="inline-formula">NO</span> production from particle precipitation.</p> <p>Our model relates the daily (longitudinally) averaged <span class="inline-formula">NO</span> number densities from SCIAMACHY <span class="cit" id="xref_paren.5">(<a href="#bib1.bibx15">Bender et al.</a>, <a href="#bib1.bibx15">2017</a><a href="#bib1.bibx15">b</a>, <a href="#bib1.bibx14">a</a>)</span> as a function of geomagnetic latitude to the solar Lyman-<span class="inline-formula"><i>α</i></span> and the geomagnetic AE (auroral electrojet) indices. We use a non-linear regression model, incorporating a finite and seasonally varying lifetime for the geomagnetically induced <span class="inline-formula">NO</span>. We estimate the parameters by finding the maximum posterior probability and calculate the parameter uncertainties using Markov chain Monte Carlo sampling. In addition to providing an estimate of the <span class="inline-formula">NO</span> content in the mesosphere, the regression coefficients indicate regions where certain processes dominate.</p>