**TL;DR:** you can't run seasonal models with a seasonality of 365 days in `ETS`. Use `stlf` instead.

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The best approach would be to specify your original time series (in your case, `precipitacaoTotal`) as seasonal. This is what you could do for monthly seasonality:

    library(fable)
    set.seed(1)
    foo <- as_tsibble(ts(rnorm(1000),frequency=12))
    foo %>% model(ETS(value~season(method="A"))) %>% forecast(h="3 years") %>% autoplot(foo)

[![forecast][1]][1]

Since I forced seasonality using `season(method="A")`, we get a (pedagogically useful, but of course nonsensical) seasonal forecast. If we let `ETS` decide on a model on white noise, it would of course not choose a seasonal one.

However, this will not work for longer periods. If we use the same idea with `frequency=365`, 

    set.seed(1)
    as_tsibble(ts(rnorm(1000),frequency=365)) %>% model(ETS(value))

we get a rather unhelpful error message (formatted):

    # A mable: 1 x 1
      `ETS(value)`
           <model>
    1 <NULL model>
    Warning message:
    1 error encountered for ETS(value)
    [1] .data contains implicit gaps in time. 
    You should check your data and convert implicit gaps into explicit
    missing values using `tsibble::fill_gaps()` if required.

I don't quite see where `ETS` sees implicit gaps, and will ping the maintainers of `fable`. 

However, the underlying reason is probably that `ETS` does not support seasonal periods longer than 24 periods: running

    set.seed(1)
    as_tsibble(ts(rnorm(1000))) %>% model(ETS(value~season(period=365)))

yields the much more informative warning message

    # A mable: 1 x 1
      `ETS(value ~ season(period = 365))`
                                  <model>
    1                        <ETS(A,N,N)>
    Warning message:
    Seasonal periods (`period`) of length greather than 24 are not supported by ETS. 
    Seasonality will be ignored. 

This is actually the same behavior as in the older `forecast` package. Running

    library(forecast)
    set.seed(1)
    ets(ts(rnorm(1000),frequency=365))

yielded (among other outputs):

    I can't handle data with frequency greater than 24.
    Seasonality will be ignored. Try stlf() if you need seasonal forecasts.

And this actually makes a lot of sense. In exponential smoothing (whether in a state space framework or otherwise), having a seasonality of $k$ periods means you need to estimate $k$ initial conditions. 365 initial conditions is *a lot*. You would overfit massively. So go with `stlf` instead.

  [1]: https://i.sstatic.net/BnjYZ.png