Temporal fluctuation scaling (TFS) is a power-law relation between the variance ($\mathrm{\ensuremath{\Xi}}$) and the mean ($\mathrm{\ensuremath{\Upsilon}}$) which is present in cumulative time series. Taking into account that Theil index ($T$) can be assumed as a measure of dispersion and considering diffusive trajectory time series, we find a power-law relation between $T$ and $\mathrm{\ensuremath{\Upsilon}}$ of the form $T\ensuremath{\sim}{(1\ensuremath{-}c\mathrm{\ensuremath{\Upsilon}})}^{\ensuremath{\beta}}$, which we call temporal Theil scaling (TTS). Specifically, by analyzing data of volatility and absolute log-return for 24 nonstationary time series of financial markets, meteorology, and COVID-19 spread, we find that TTS is present in diffusive trajectory time series, while TFS is not present. Furthermore, we show that the power-law relation of TTS has a form that is similar to the relation between order parameter and temperature, which is found in the Ginzburg-Landau theory when the nontrivial critical points of an energy functional ${\mathcal{F}}_{\ensuremath{\eta},\ensuremath{\delta}}$ containing arbitrary powers $\ensuremath{\eta}$ and $\ensuremath{\delta}$ of the order parameter are calculated.